Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction

Deep Learning based hybrid residual attention and... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/14-650" }, "headline": "Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease...", "datePublished": "2025-07-03T14:36:54", "dateModified": "2025-09-16T15:53:50", "author": [ { "@type": "Person", "name": "Cenitta D" }, { "@type": "Person", "name": "VIijaya Arjunan Ranganathan" }, { "@type": "Person", "name": "Tanuja Shailesh" }, { "@type": "Person", "name": "Andrew J" }, { "@type": "Person", "name": "Arul N" }, { "@type": "Person", "name": "Praveen Pai T" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Early and accurate prediction of ischemic heart disease (IHD) is essential for reducing mortality and enabling timely intervention. Misdiagnosis can lead to severe health outcomes, emphasizing the need for robust and intelligent predictive models. Deep learning approaches have shown strong potential in identifying hidden patterns in medical data and aiding clinical decision-making. Methods This study proposes a novel Hybrid Residual Attention with Echo State Network (HRAESN) model that integrates Attention Residual Learning (ARL) with Echo State Networks (ESN) to enhance feature extraction and temporal data learning. The hybrid model is designed to refine feature attention through residual learning while leveraging ESN for efficient time-series prediction. Two publicly available benchmark datasets were used for evaluation: the Kaggle Cardiovascular Disease dataset comprising 70,000 instances and the UCI Heart Disease dataset containing 303 instances. Missing values in both datasets were handled using a multiple imputation technique tailored for ischemic heart disease. Model performance was assessed using standard classification metrics, including accuracy, sensitivity, specificity, precision, recall, and F-measure. Results The proposed HRAESN model demonstrated superior classification performance compared to traditional machine learning and deep learning approaches. It achieved an accuracy of 98.4% on the Kaggle dataset and 97.7% on the UCI dataset. Additionally, the model showed high sensitivity and specificity, indicating strong diagnostic capability and reliability in identifying both diseased and non-diseased cases. Conclusions The HRAESN model effectively combines the strengths of residual attention mechanisms and echo state networks, resulting in improved accuracy and stability for ischemic heart disease prediction. Its strong performance on benchmark datasets confirms its potential as a valuable clinical decision support tool for early detection of IHD. Future work may focus on optimizing model complexity and integrating real-time medical IoT data to enhance practical deployment in healthcare systems. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-650/v1", "name": "Deep Learning based hybrid residual attention and echo state network..." } } ] } Home Browse Deep Learning based hybrid residual attention and echo state network... ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article D C, Ranganathan VA, Shailesh T et al. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.12688/f1000research.165575.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] Cenitta D https://orcid.org/0000-0003-3715-6941 1 , VIijaya Arjunan Ranganathan https://orcid.org/0000-0002-1402-6573 1 , Tanuja Shailesh 1 , Andrew J 1 , Arul N 2 , Praveen Pai T 1 Cenitta D https://orcid.org/0000-0003-3715-6941 1 , VIijaya Arjunan Ranganathan https://orcid.org/0000-0002-1402-6573 1 , [...] Tanuja Shailesh 1 , Andrew J 1 , Arul N 2 , Praveen Pai T 1 PUBLISHED 03 Jul 2025 Author details Author details 1 Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 2 Computer Science and Engineering, AJ Institute of Engineering and Technology, Mangalore, Karnataka, India Cenitta D Roles: Methodology, Project Administration VIijaya Arjunan Ranganathan Roles: Conceptualization, Writing – Review & Editing Tanuja Shailesh Roles: Writing – Review & Editing Andrew J Roles: Data Curation Arul N Roles: Visualization Praveen Pai T Roles: Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. This article is included in the Manipal Academy of Higher Education gateway. Abstract Background Early and accurate prediction of ischemic heart disease (IHD) is essential for reducing mortality and enabling timely intervention. Misdiagnosis can lead to severe health outcomes, emphasizing the need for robust and intelligent predictive models. Deep learning approaches have shown strong potential in identifying hidden patterns in medical data and aiding clinical decision-making. Methods This study proposes a novel Hybrid Residual Attention with Echo State Network (HRAESN) model that integrates Attention Residual Learning (ARL) with Echo State Networks (ESN) to enhance feature extraction and temporal data learning. The hybrid model is designed to refine feature attention through residual learning while leveraging ESN for efficient time-series prediction. Two publicly available benchmark datasets were used for evaluation: the Kaggle Cardiovascular Disease dataset comprising 70,000 instances and the UCI Heart Disease dataset containing 303 instances. Missing values in both datasets were handled using a multiple imputation technique tailored for ischemic heart disease. Model performance was assessed using standard classification metrics, including accuracy, sensitivity, specificity, precision, recall, and F-measure. Results The proposed HRAESN model demonstrated superior classification performance compared to traditional machine learning and deep learning approaches. It achieved an accuracy of 98.4% on the Kaggle dataset and 97.7% on the UCI dataset. Additionally, the model showed high sensitivity and specificity, indicating strong diagnostic capability and reliability in identifying both diseased and non-diseased cases. Conclusions The HRAESN model effectively combines the strengths of residual attention mechanisms and echo state networks, resulting in improved accuracy and stability for ischemic heart disease prediction. Its strong performance on benchmark datasets confirms its potential as a valuable clinical decision support tool for early detection of IHD. Future work may focus on optimizing model complexity and integrating real-time medical IoT data to enhance practical deployment in healthcare systems. READ ALL READ LESS Keywords UCI, Kaggle, Heart Disease, Imputation, Deep Learning, Echo State Network, Residual Attention. Corresponding Author(s) VIijaya Arjunan Ranganathan ( [email protected] ) Tanuja Shailesh ( [email protected] ) Close Corresponding authors: VIijaya Arjunan Ranganathan, Tanuja Shailesh Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 D C et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: D C, Ranganathan VA, Shailesh T et al. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.12688/f1000research.165575.1 ) First published: 03 Jul 2025, 14 :650 ( https://doi.org/10.12688/f1000research.165575.1 ) Latest published: 16 Sep 2025, 14 :650 ( https://doi.org/10.12688/f1000research.165575.2 )  There is a newer version of this article available. Suppress this message for one day. 1. Introduction People who develop Ischemic heart disease experience limited blood circulation within specific parts of their body structure. The reduced flow of blood along with diminished oxygen levels to the heart muscle causes cardiac ischemia mostly because of blocked coronary arteries. The persistent reduction of oxygen delivery to the heart through coronary arteries results in coronary artery disease or coronary heart disease leading to heart attack development. Silent ischemia affects numerous individuals who endure heart blood flow interruptions which occur without showing any indicators. Such individuals face risk of experiencing sudden cardiac events easily. The occurrence of silent ischemic events is more frequent in diabetic patients and in individuals who have suffered heart attacks previously. Standard diagnostic techniques consisting of stress tests and Holter monitoring assist medical practitioners in detecting this condition. The Holter monitor represents a portable ECG tool with built-in battery power which tracks heart activity throughout 24 to 48 hours to identify blood flow irregularities. The severity of symptoms determines what diagnostic tests will be used for the evaluation. 1 The World Health Organization (WHO) reports that cardiovascular diseases (CVDs) continue as the main cause of global mortality since 17.9 million people died from CVDs in 2019 which amounted to 32% of worldwide fatalities. Heart attacks and strokes lead to 85% of fatal outcomes among the tested patients. 2 The worldwide fatalities from noncommunicable diseases reached 17 million during 2019 before people turned 70 years old and cardiovascular conditions caused 38% of those premature deaths. Medical detection of CVDs remains vital because behavioral prevention through risk control methods such as smoking and food control and weight management cannot substitute for early medical discovery to achieve both effective treatment and lower mortality rates. Heart disease poses a major financial challenge and increasing health burden because of high surgical expenses and rising population incidence mainly affecting developing countries. Knowledge about how patient characteristics link to heart disease risk serves as the basis for preventing the condition and detecting it early for treatment purposes. Deep learning has become an integral part of computer vision, object recognition, natural language processing, speech recognition, medical diagnostics, bioinformatics, and drug discovery. Similar to traditional artificial neural networks (ANNs), deep learning models consist of input, hidden, and output layers, with patient risk factors serving as input features. The research demonstrates that artificial neural networks deliver outstanding results when used for identifying and foretelling coronary heart disease. 3 Medical AI applications experience rapid growth because of three main factors including Internet of Things (IoT) and powerful computing hardware (e.g., GPUs and TPUs) together with big medical datasets. Essential information needed by deep learning models comes from Medical IoT devices together with electronic health records as well as genomic data and central medical databases. The critical challenges include preserving data privacy as well as successfully deploying the models and optimizing service quality despite their importance. 3 Time-series prediction has seen increased popularity among researchers who use recurrent neural networks (RNNs) as deep learning-based approaches. RNNs work with sequential data sets through the process of feeding output data from previous components to next steps making them ideal for ECG signal processing and patient health surveillance. RNNs differ from regular neural networks by retaining previous input data thus they produce enhanced forecasts for temporal information patterns. Traditional RNNs experience gradient vanishing problems because of which they become problematic for handling long sequences. The development of both Hochreiter and Schmidhuber led to long short-term memory (LSTM) networks which incorporated memory gates to control information transmission and suppress gradient deterioration. 4 Time-series extrapolation along with fast learning occurs efficiently through Echo State Networks (ESN) which function as a preferred substitute to normal RNNs. 5 An Echo State Network functions through its reservoir of recurrent neurons connected haphazardly that helps the network learn complex patterns yet uses few processing resources. The forecast capabilities of time-series prediction and representation learning capabilities improve through the use of Deep ESNs (DESNs) that include multiple serially connected reservoirs. 6 A transformation of conventional convolutional neural networks (CNNs) called Residual Attention Network brings attention mechanism integration for feature enhancement. 7 The advanced feed-forward framework permits end-to-end training which enables it to learn hierarchical features independently. Gremlin Deep Residual Attention Networks provide an efficient mechanism for deep learning systems to reach hundreds of layers through their implementation of Attention Residual Learning (ARL). 8 Different algorithms can achieve maximum strength performance through hybrid deep learning models which integrate multiple techniques. Medical diagnostic accuracy along with efficiency can experience significant improvement by combining residual attention learning methods with Echo State Networks. The appropriate addressing of missing values through the Ischemic Heart Disease Multiple Imputation Technique creates improved data reliability and completeness. 9 1.1 Objective of this study The main goal of this research work is to create a Hybrid Residual Attention with Echo State Network (HRAESN) model used to predict ischemic heart disease (IHD) at an early stage while maintaining high accuracy. The proposed method integrates Residual Attention Learning (RAL) with Echo State Networks (ESNs) to boost both feature extraction and time-series classification and general model performance. This study solves data preprocessing problems with Ischemic Heart Disease Multiple Imputation Technique while using hybrid deep learning effectively for robust classification. The research uses two recognized heart disease data sets including 70,000 records from the Kaggle Cardiovascular Disease dataset and 303 records from the UCI Heart Disease dataset to evaluate the proposed method. The objective is to prove that this approach outperforms current state-of-the-art heart disease prediction methods. ART-based analysis findings will enhance clinical diagnosis along with IHD detection and patient care through AI-powered diagnostic systems. The following research questions are the focus of the study’s search and synthesis of the literature. 1. How do deep learning models, particularly Echo State Networks (ESNs) and Residual Attention Learning (RAL), improve the accuracy and stability of ischemic heart disease prediction compared to traditional machine learning approaches? 2. What are the key challenges associated with handling missing data in medical datasets, and how can the Ischemic Heart Disease Multiple Imputation Technique enhance data completeness and reliability? 3. How does the proposed Hybrid Residual Attention with Echo State Network (HRAESN) model perform on benchmark datasets (Kaggle Cardiovascular Disease and UCI Heart Disease) compared to existing state-of-the-art heart disease prediction models? 1.2 Problem statement One of the main causes of death is ischemic heart disease (IHD), which needs to be predicted early and accurately in order to be effectively treated. While current machine learning models have trouble managing missing data, time-series dependencies, and computational inefficiencies, traditional diagnostic techniques are costly, time-consuming, and rely on expert interpretation. Vanishing gradients and high complexity are two drawbacks of deep learning techniques like recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. To address these challenges, this study proposes a Hybrid Residual Attention with Echo State Network (HRAESN) model, integrating Residual Attention Learning (RAL) for feature extraction and Echo State Networks (ESNs) for efficient time-series processing, ensuring improved predictive accuracy and robustness. 2. Related works Scientific studies have evaluated multiple deep learning and machine learning prediction methods for heart disease since the turn of the century. The use of recent hybrid deep learning models leads to improved IHD diagnosis accuracy by incorporating various learning techniques. These diagnostic techniques strive to identify the disease at an early stage to enhance medical choices made by healthcare professionals. The research by Li et al. 10 presented an S-shaped reconstruction model for arrhythmia detection which employed a 2D 19-layer deep squeeze-and-excitation residual network for predicting heartbeat rates. The authors showed through their work that S-shaped reconstruction demonstrated effective extraction of vital features in ECG heartbeat signals. Self-supervised learning needs additional evolution to complete the enhancement of model classification effectiveness. Their method enables improved feature extraction through graph-based learning which works without needing complete understanding about graph structure in advance. 11 Ruobin et al. 5 designed a two-stage heart disease forecasting system which combines Empirical Wavelet Transformation (EWT) with Echo State Network (ESN). Empirical Wavelet Transformation-ESN models validated their superiority over traditional forecasting methods during their experimental research. RAGCN serves as the title of Bing et al.’s 12 study which developed a classification method for internet services based on Residual Attention Graph Convolutional Network technology featuring attention mechanisms to dynamically weight neighboring nodes yet maintain efficient operation. Sun et al. 13 developed the Deep Belief Echo-State Network (DBEN) which utilized Deep Belief Networks (DBN) for feature extraction and Echo State Networks (ESN) for fast learning operations during time series prediction. Short-term memory capacity increased and learning speed and prediction accuracy improved when using DBEN according to their recorded results. The process of achieving optimal parameters for DBEN represents an unresolved issue. Anguo et al. 14 developed a high-precision computing system for time series classification through their work that employed deep CNN models together with reservoir computing and spike encoding features. 15 , 16 The researchers at Qiang et al. 17 introduced a Deep Bidirectional Echo State Network (DBESN) to execute forecasting tasks using scarce data. The proposed system combines Deep Autoencoder Echo State Networks (DAESN) with Deep Bidirectional State Echo State Networks (DBSESN) to find forward and backward time-scale features. Ren et al. implemented a Divided Adaptive Multi-Objective Differential Evolution (DAMODE) classifier that optimizes ESN reservoir constraints resulting in excellent generalizability and classification accuracy according to. 18 , 19 The researchers at Anusha et al. 20 created a deep learning system using DBNs and RNNs for making features and categories. The authors implemented feature weight optimization using Jaya Algorithm-based Multi-Verse Optimization (JA-MVO) technology to surpass previous versions of the model. The research of Chandrasekaran et al. 21 created an on-chip mixed-signal Echo State Network for detecting early cardiac diseases. 21 The low-power model of their ESN outperformed deep neural networks (DNNs) in terms of processing efficiency. By integrating Deep Residual Network with Attention Mechanisms Liu et al. 22 developed a system for detecting heart diseases in ECG signals through ensemble learning to boost classification results. Chunyan et al. 23 designed Recursion-Enhanced Random Forest with an Improved Linear Model (RFRF-ILM) to integrate various feature combinations for an improved heart disease prediction accuracy. Real-time CVD monitoring presents itself as an advantageous application of the Internet of Medical Things according to their research. The research study led by Tamilarasi et al. developed a cardiac disease classification model that unites Random Forest (RF) and Support Vector Machines (SVM) technologies. Their approach for achieving improved forecast accuracy included a feature elimination process that repeated continuously. SVM models are affected by the hyperparameter C and gamma values which leads to instability problems according to their study. 24 Girish et al. 25 presented a combined deep hybrid model consisting of RNN and LSTM architecture for heart disease diagnosis. Their work employed cross-validation methodologies & data preprocessing strategies to handle intractable data to receive better results of classification rate compared to individual use of ML tools. A hybrid clustering technique that uses; ECG signals and numerical data was proposed by Ritesh et al. 26 for predicting cardiac disease. They introduced a new approach which enhanced prediction accuracy through a merged approach of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and the optimized K-Means Clustering (KMC). Though, their protocol had less specificity than other methods suggest that future studies should look for extra hybrid clustering methods that could be more precise. Scientific teams have implemented state-of-the-art machine learning algorithms to develop hybrid detection systems that evaluate heart disease through medical data and ECG signals. The predictive accuracy of heart disease diagnosis has been improved by Naïve Bayes (NB) and Random Forest (RF) and Restricted Boltzmann Machines (RBM) techniques according to research findings. 27 , 28 Decision Tree (DT) classification models have been used to enhance heart disease prediction accuracy according to studies in reference. 29 The research of Ritesh et al. 30 brought forward a heart disease classification system which merged K-Means as distance-based clustering with DBSCAN as density-based clustering. The hybrid clustering technique achieved better forecasting outcomes than standalone clustering procedures through their implementation. 3. Materials and methods 3.1 Dataset This study utilizes data from two publicly available repositories: Kaggle and the UCI (University of California, Irvine) Machine Learning Repository. These datasets provide comprehensive patient records used for cardiovascular disease prediction and ischemic heart disease classification. 3.1.1 Kaggle cardiovascular disease dataset There are 70,000 patient records with 11 distinct features in the Kaggle Cardiovascular Disease dataset. 31 When medical practitioners performed clinical examinations, these characteristics were noted. Three types types of input features make up the dataset: 1. Objective Characteristics (Real patient data): Gender, Age, Height, and Weight 2. Features of the Examination (Medical Test Results): Blood Pressure Systolic and Diastolic, Blood Pressure Levels of Cholesterol and Glucose 3. Subjective Features (patient data as self-reported): Alcohol use, smoking, and physical activity 3.1.2 UCI heart disease dataset The UCI Heart Disease dataset contains 76 features, of which 14 are highly relevant for heart disease diagnosis. 32 The predictive class attribute is typically listed last, indicating the presence or absence of heart disease. Table 1 and Table 2 provide detailed descriptions of the dataset attributes. Table 1. Kaggle cardiovascular disease dataset description. Attribute Description Age Objective Feature|age|int (days) Height Objective Feature|height|int (cm)| Weight Objective Feature|weight|float (kg)| Gender Objective Feature|gender|categorical code| Systolic blood pressure Examination Feature|ap_hi|int| Diastolic blood pressure Examination Feature|ap_lo|int| Cholesterol Examination Feature|cholesterol| 1: normal, 2: above normal, 3: well above normal Glucose Examination Feature|gluc| 1: normal, 2: above normal, 3: well above normal Smoking Subjective Feature|smoke|binary| Alcohol intake Subjective Feature|alco|binary| Physical activity Subjective Feature|active|binary| Presence or absence of cardiovascular disease Target Variable|cardio|binary| Table 2. UCI heart disease dataset description. Attribute Description Domain of value Age Age in year 29 to 77 Sex Sex Male (1) Female (0) Cp Chest pain type Typical angina (1) Atypical angina (2) Non-anginal (3) Asymptomatic (4) Trestbps Resting blood sugar 94 to 200 mm Hg Chol Serum cholesterol 126 to 564 mg/dl Fbs Fasting blood sugar >120 mg/dl True (1) False (0) Restecg Resting ECG result Normal (0) ST-T wave Abnormality (1) LV hypertrophy (2) Thalach Maximum heart rate achieved 71 to 202 Exang Exercise induced angina Yes (1) No (0) Oldpeak ST depression induced by exercise relative to rest 0 to 6.2 Slope Slope of peak exercise ST segment Upsloping (1) Flat (2) Downsloping (3) Ca Number of major vessels coloured by fluoroscopy 0 – 3 Thal Defect type Normal (3) Fixed defect (6) Reversible defect (7) Num Heart disease 0-4 3.1.3 Datasets and ethical considerations This study utilizes two publicly available datasets: the Heart Disease dataset from the UCI Machine Learning Repository and the Cardiovascular Disease dataset from Kaggle. These datasets contain anonymized patient records and are publicly released for academic and research purposes. 3.1.4 Ethical approval statement As this research involves only the use of publicly accessible, anonymized datasets, no formal ethical approval was required. The study complies with the ethical principles outlined in the Declaration of Helsinki. No intervention or interaction with human subjects occurred. 3.1.5 Informed consent statement Because this study used pre-existing anonymized data from public repositories, informed consent from participants was not required. All necessary ethical permissions and participant consents were obtained by the original data providers as per their respective institutional and data-sharing policies. 3.2 Hybrid data classification algorithm The classification of ischemic heart disease (IHD) in this study is based on a hybrid deep learning model that integrates machine learning (ML), soft computing techniques, and optimization methods to enhance accuracy and robustness. Different classification models are created by integrating various ML methods and ensemble learning methods that involve bagging and boosting. Multiple classifiers work together in ensemble methods to generate better generalization as well as decrease overfitting. HRAESN model combines the following key elements: 1. Echo State Networks (ESNs) for efficient time-series processing 2. Attention Residual Learning (ARL) for enhanced feature extraction By combining ESN and ARL, the model achieves higher accuracy, better generalization, and improved stability compared to conventional ML classifiers. 3.3 Echo State Network (ESN) Echo State Networks (ESNs), a subset of recurrent neural networks (RNNs) created for effective sequential data processing, are a part of the reservoir computing paradigm. In contrast to conventional RNNs, an ESN’s hidden layer (reservoir) is fixed and randomly initialized, whereas only the output layer is trained. Key features of ESNs include: • The reservoir exhibits two weight sets which are fixed by random values without training: W_in for input-to-lateral connections and W_r for lateral connections. • During ESN operation researchers only train output weights but maintain simple computational design for efficient pattern learning capability. • The hidden layer connectivity of ESNs remains sparse which decreases computational complexity. • Nonlinear Embedding: The reservoir state provides a nonlinear transformation of input data, which can then be mapped to the desired output using a trainable readout layer. Since ESNs retain past information in a fixed reservoir, they are highly effective for time-series forecasting and real-time signal processing, making them a suitable choice for ischemic heart disease prediction. 3.4 Attention Residual Learning (ARL) Attention Residual Learning (ARL) is a deep learning technique that enhances feature extraction by selectively focusing on relevant information while reducing noise in deep neural networks. It is particularly beneficial in medical image analysis and time-series classification. Key challenges in deep residual networks include: • Performance Degradation: Stacking multiple narrow attention modules can lead to a decline in performance. • Feature Suppression: Soft mask layers may inadvertently reduce the importance of relevant features. To address these issues, ARL modifies feature representation using an attention mask. The transformation is mathematically represented as: H i ( t + 1 ) = ( 1 + M i ( t ) ) ∗ X F i ( t ) Where: i: Index position in the input matrix M i (t): Gradient of the input feature mask during the t-th iteration H i (t+1): Updated attention module output at the (t+1)-th iteration This formulation ensures that: 1. Relevant features are amplified, while irrelevant features are suppressed. 2. Deep residual networks maintain stable performance even with hundreds of layers. 3. Computational efficiency is preserved without significantly increasing model complexity. The integration of ESNs with ARL enables the proposed HRAESN model to merge its time-series learning functionality with attention-based feature refinement that results in precise and stable outcomes for ischemic heart disease predictions. 3.5 Methodology The prediction model utilizes heart disease records from UCI Heart Disease Data Set and the Cardiovascular Disease dataset from Kaggle. Pre-processing starts with performing the Ischemic Heart Disease Multiple Imputation Technique to identify and imputation missing values before proceeding further. 1 The HRAESN model combines Echo State Networks (ESNs) for short-term memory processing with Attention Residual Learning (ARL) for enhancing features to classify heart disease. The model’s accuracy, sensitivity, and specificity are assessed using a confusion matrix. The experiment’s process is shown in Figure 1 , and the HRAESN classifier’s overall system model is shown in Figure 2 . Figure 1. Workflow of the proposed experiment using the UCI heart disease and Kaggle cardiovascular disease datasets. Figure 2. Overall system model of the Hybrid Residual Attention with Echo State Network (HRAESN). Experiment workflow 1. Load and preprocess datasets: The Heart Disease Data Set and Cardiovascular Disease dataset are loaded, and missing values are imputed using the Ischemic Heart Disease Multiple Imputation Technique. 9 , 33 2. Feature extraction and classification: The HRAESN model applies ESNs for sequence modeling and ARL for refining feature representation. 3. Model evaluation: A confusion matrix assesses the model’s performance, ensuring accurate classification of heart disease cases. 3.5.1 Hybrid Residual Attention with Echo State Network (HRAESN) algorithm The input feature matrix (X F ) is obtained from the Ischemic Heart Disease Multiple Imputation Technique and labeled according to class 0 (normal) or class 1 (heart disease). Echo State Network (ESN) Hidden Layer Dynamics (1) X F ( t + 1 ) = f a ( W i u ( t ) + W r X F ( t ) ) Where: • X F ( t + 1 ) and X F ( t ) are the feature matrices at iterations t and t + 1. • W i is the input reservoir weight matrix derived from the input data. • W r is the reservoir weight matrix representing internal states. • u ( t + 1 ) represents the internal states computed at iteration t. • f a ( . ) is the activation function applied at the reservoir. Attention Residual Learning (ARL) transformation (2) H i ( t + 1 ) = ( 1 + M i ( t ) ) ∗ X F i ( t ) Where: • i represents the input matrix’s index positions. • M i ( t ) is the gradient of the input feature mask at iteration t. • H i ( t + 1 ) is the attention module output at iteration t + 1. The reservoirs in HRAESN are linked in series, meaning each reservoir state depends on the previous reservoir’s output and its own past state: (3) X F 1 ( t + 1 ) = f a ( W i u ( t ) + W 1 X F 1 ( t ) ) (4) X F 2 ( t + 1 ) = f a ( W i X F 1 ( t ) + W 2 X F 2 ( t ) ) (5) X F M ( t + 1 ) = f a ( W i X F ( M − 1 ) ( t ) + W M X F M ( t ) ) Where: • W i = H i ( t + 1 ) represents the attention module output. Activation Functions and Output Computation 1. Final Activation Function (6) A n = Y L · sigmoid ( X F M ( t + 1 ) ) Where: • sigmoid ( . ) is the activation function applied to the final output layer. Dynamic Echo State Network Output (7) P R ( t + 1 ) = g a ( W o X F M ( t + 1 ) ) Where: • W o represents the output reservoir weight matrix. • g a ( . ) is the final activation function used at step 4. Algorithm. Hybrid Residual Attention with Echo State Network (HRAESN). Input: features data X F , label data Y L Output: Predicted result P r 1: begin 2: for each Compute the Hidden layer of dynamic ESN 3: X F ( t + 1 ) = f a ( W i u ( t ) + W r X F ( t ) ) 4: end for 5: for each compute the attention residual learning 6: H i ( t + 1 ) = ( 1 + M i ( t ) ) ∗ X F i ( t ) 7: end for 8: for x=1 to M do: 9: X F 1 ( t + 1 ) = f a ( W i u ( t ) + W 1 X F 1 ( t ) ) 10: X F 2 ( t + 1 ) = f a ( W i X F 1 ( t ) + W 2 X F 2 ( t ) ) 11: … 12: X F M ( t + 1 ) = f a ( W i X F M − 1 ( t ) + W M X F M ( t ) ) 13: end 14: end 3.5.2 Hyperparameter tuning The Hyperparameter Tuning process optimizes the performance of the Hybrid Residual Attention with Echo State Network (HRAESN) model by carefully selecting key parameters for both Echo State Networks (ESN) and Attention Residual Learning (ARL). The reservoir size (500 neurons) and spectral radius (0.8) ensure stable memory retention for time-series processing, while 10% sparse connectivity enhances computational efficiency. The input scaling (0.5) and leaky rate (0.2) regulate data flow within the reservoir, preventing overfitting. The attention module depth (3 layers) and mask range ([0,1]) refine feature selection, improving model interpretability. The model is trained using the Adam optimizer with a learning rate of 0.001, a batch size of 32, and 100 epochs for optimal convergence. The model prevents overfitting through dropout rate 0.3 while 80:20 train-test split maintains evaluation stability. The optimized parameters lead to precise and efficient and stable ischemic heart disease predictions as described in Table 3 . Table 3. Summary of hyperparameter settings used in the proposed model training. Parameter Value Description Number of Reservoir Neurons (N_res) 500 Number of neurons in the ESN reservoir. Determines the capacity of the reservoir to store and process sequential information. Spectral Radius (ρ) 0.8 Controls the stability of the ESN. A value < 1 ensures echo state property for long-term memory. Reservoir Connectivity (%) 10% Percentage of nonzero connections in the reservoir matrix W r , ensuring sparse connectivity. Input Scaling (W_in) 0.5 Determines how input data is mapped into the reservoir. Leaky Rate (α) 0.2 Defines how much of the previous state is retained in the ESN for time-series processing. Readout Regularization (λ) 10 −4 Ridge regression parameter to prevent overfitting in the output layer of ESN. Attention Module Depth 3 Number of stacked attention modules in ARL to enhance feature learning. Attention Mask Range (M_i (t)) [0,1] Defines the range of soft masks applied in attention residual learning. Activation Function (f_a(.)) Tanh Non-linear activation function used in the ESN reservoir. Output Activation Function (g_a(.)) Sigmoid Activation function used in the final output layer to predict class labels. Batch Size 32 Number of training samples processed before updating model weights. Optimizer Adam Optimization algorithm used to update model parameters. Learning Rate (η) 0.001 Controls the step size of weight updates during training. Dropout Rate 0.3 Fraction of neurons randomly dropped during training to prevent overfitting. Number of Epochs 100 Total number of times the model iterates over the entire dataset during training. Train-Test Split Ratio 80:20:00 Data split for training (80%) and testing (20%). 4. Results and analysis To predict the existence of ischemic heart disease (IHD), a number of classification methods were employed, including Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and AdaBoost. Data from the Cardiovascular Disease dataset (Kaggle) and the Heart Disease Data Set (UCI) were used in the experiments. 4.1 Experiment setup and data preprocessing The datasets contain various medical indicators that serve as input features for classification. The target variable is binary: • Class 1: Presence of ischemic heart disease • Class 0: Absence of disease The proposed hybrid HRAESN model is trained using 80% of the dataset, and the remaining 20% is used for testing. Principal Component Analysis (PCA) was applied to highlight variance and distinct patterns in the dataset. Figure 3 shows the PCA plot, where: • Principal Component 1 (X-axis) and Principal Component 2 (Y-axis) capture most of the variance. • Blue (0) represents healthy individuals, while Red (1) represents patients with heart disease. Figure 3. PCA plot showing data distribution in the heart disease dataset based on the first two principal components. Additionally, six records in the UCI dataset had missing values, which were imputed using the Ischemic Heart Disease Multiple Imputation technique, producing a complete dataset with no missing values. 4.2 Experimental results Tables 4 and 5 present the normalized confusion matrix for the HRAESN model using the UCI Heart Disease dataset and Kaggle Cardiovascular Disease dataset, respectively. Table 4. Normalized confusion matrix for the Hybrid Residual Attention with Echo State Network (HRAESN) using the UCI heart disease datasets. Predicted Label Class 0 1 Actual label 0 163 1 1 3 136 Table 5. Normalized confusion matrix for the hybrid residual attention with Echo state network using Kaggle cardiovascular disease dataset. Predicted Label Class 0 1 Actual label 0 34431 549 1 568 34452 Figures 4 – 6 illustrate the comparative performance of different classifiers used for ischemic heart disease prediction. Figure 4 evaluates Sensitivity, Specificity, Precision, F-measure, and Accuracy, providing insight into the model’s ability to correctly classify positive and negative cases. Higher values indicate improved diagnostic reliability. 34 Figure 5 presents the Kappa coefficient, Recall, and Jaccard coefficient, which measure classifier agreement beyond chance, model recall capability, and overall similarity between predicted and actual values. A higher Kappa score signifies better classifier consistency. Figure 4. Analysis of classifier performance based on sensitivity, specificity, precision, F-measure, and accuracy. Figure 5. Analysis of classifier performance using Kappa coefficient, Recall, and Jaccard coefficient on UCI heart disease and Kaggle cardiovascular disease datasets. Figure 6 examines Classification Error Rate, False Acceptance Rate (FAR), and False Rejection Rate (FRR), assessing the model’s robustness against false classifications. A lower FAR and FRR indicate reduced misclassification, ensuring better clinical applicability. These evaluations confirm that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. Figure 6. Classification error rate, false acceptance rate, and false rejection rate for UCI heart disease and Kaggle cardiovascular disease datasets. 4.3 Comparative analysis with existing models Tables 6 and 7 present a comparative analysis between the proposed Hybrid Residual Attention with Echo State Network (HRAESN) model and existing heart disease prediction models. The comparison is based on handling of missing values, classifier types, and accuracy performance across different studies. Unlike traditional models that either delete missing data or use basic imputation techniques, the HRAESN model applies a multiple imputation approach, ensuring data completeness and improving prediction reliability. The results indicate that the HRAESN model outperforms previous approaches, achieving 97.71% accuracy on the UCI Heart Disease dataset and 98.4% accuracy on the Kaggle Cardiovascular Disease dataset. Compared to Random Forest (RF), Gradient Boosting (GB), Multilayer Perceptron (MLP), and other ensemble methods, the HRAESN model exhibits superior classification performance, demonstrating its effectiveness in early ischemic heart disease detection and clinical decision support. Table 6. Comparison of HRAESN with existing methods using the UCI heart disease dataset. Study Year Handling of missing values Classifiers Accuracy (%) Jabbar et al. 35 2016 Rows with missing values deleted RF 83.6 Verma & Mathur 36 2019 Rows with missing values deleted MLP 85.48 Latha & Jeeva 37 2019 Rows with missing values deleted Hybrid NB, BN, MLP, RF 85.48 Tama et al. 38 2020 Rows with missing values deleted Two-tier ensemble (RF, GB, XGBoost) 85.71 Pooja et al. 34 2021 MICE Algorithm RF 86.6 Proposed HRAESN 2023 Multiple Imputation Technique HRAESN 97.71 Table 7. Comparison of HRAESN with existing methods using the Kaggle cardiovascular disease dataset. Study Year Classifiers Accuracy (%) Maiga et al. 39 2019 RF 73 Hagan 40 2021 RF, Gradient Boosting 74 Bhoyar 41 2021 MLP 89.7 Theerthagiri 42 2022 Gradient Boosting 89.7 Mohammed et al. 43 2021 Hybrid RF, NB, GB 94 Proposed HRAESN 2023 HRAESN 98.4 Table 8. Performance comparison of different algorithms. Classifiers Accuracy Specificity Sensitivity Logistic regression 83.3 82.3 86.3 K neighbors 84.8 77.7 85 SVM 83.2 78.7 78.2 RF 80.3 78.7 78.2 DT 82.3 78.9 78.5 Deep Learning 94.2 83.1 82.3 Proposed HRAESN with UCI dataset 97.71 98.03 97.4 Proposed HRAESN with Kaggle dataset 98.4 98.42 98.37 Figure 7 compares the HRAESN model with Residual Networks (ResNet) and Echo State Networks (ESN) in terms of classification performance. The HRAESN model achieves 0.98, significantly outperforming ESN (0.89) and ResNet (0.75). This improvement demonstrates the effectiveness of combining Echo State Networks with Attention Residual Learning, enhancing feature extraction and time-series prediction. The results confirm that HRAESN provides superior accuracy and stability in ischemic heart disease classification. Figure 7. Comparison of residual network, echo state network, and the proposed Hybrid Residual Attention Echo State Network. 5. Discussion The proposed HRAESN model significantly outperforms conventional machine learning and deep learning techniques in ischemic heart disease classification. It achieves higher accuracy, sensitivity, and specificity, as demonstrated in Tables 6 – 9 . The proposed model exhibits: • Improved classification accuracy (97.71% – UCI dataset, 98.4% – Kaggle dataset) • Effective handling of missing data using Multiple Imputation Technique • Enhanced feature learning through Attention Residual Learning (ARL) • Better time-series processing with Echo State Networks (ESN) Table 9. Performance of deep learning classifiers on the heart disease dataset. DL classifiers Accuracy (%) Multi-layer perceptron 72.52 Deep neural network (200 epochs) 80.21 Recurrent neural network 88.52 Long sort term memory network 86.88 Hybrid deep learning model (RNN + LSTM) 95.1 Proposed HRAESN 97.71 However, the model has higher computational complexity, which can be optimized in future work. Integrating IoT-based medical devices for real-time heart disease monitoring can further enhance its applicability in healthcare solutions. 6. Conclusion Using the UCI Heart Disease dataset and the Kaggle Cardiovascular Disease dataset, the suggested Hybrid Residual Attention with Echo State Network (HRAESN) model has been compared to several Machine Learning (ML) and Deep Learning (DL) techniques for the classification of Ischemic Heart Disease (IHD). The experimental results demonstrate that HRAESN outpaces existing heart illness prediction methods because it achieves accuracy rates of 98.4% on Kaggle data and 97.7% on UCI data. The HRAESN model demonstrates superior performance in terms of sensitivity together with specificity and recall along with accuracy and F-measure according to deep learning model comparisons. The Ischemic Heart Disease Multiple Imputation Technique incorporated within the model succeeds in handling missing values to achieve better data completeness along with improved predictive reliability. The HRAESN model demonstrated better testing stability characteristics than conventional classifiers thus establishing itself as a dependable instrument for medical diagnosis and clinical decisions. The model achieves powerful medical dataset pattern detection through the combination of Echo State Networks (ESN) and Attention Residual Learning (ARL) features. The future research should work on optimizing the computational operations and integrating IoT-based medical equipment to detect ischemic heart disease in real-time. This approach demonstrates significant value for healthcare improvements by providing early medical diagnosis together with decreased chances of life-threatening cardiac events. Ethical statement This study did not involve human or animal subjects, and thus no ethical approval was required. CRediT authorship contribution statement D. Cenitta: Methodology and Project administration. R. Vijaya Arjunan: Conceptualization, Writing – review & editing. Tanuja Shailesh: Writing – review & editing. Andrew J: Data curation. N. Arul: Visualization. Praveen Pai T: Review & editing. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Disclaimer/publisher’s note The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s). Data availability All datasets used in this study are publicly available and were accessed under open licenses permitting reuse. The Heart Disease dataset was obtained from the UCI Machine Learning Repository and can be accessed at: https://archive.ics.uci.edu/ml/datasets/Heart+Disease Persistent Identifier : UCI Heart Disease Dataset – DOI: Not applicable (repository does not assign DOI) The Cardiovascular Disease dataset was obtained from Kaggle and can be accessed at: https://www.kaggle.com/datasets/sulianova/cardiovascular-disease-dataset Persistent Identifier : Kaggle Dataset – DOI: Not applicable (repository does not assign DOI) All data supporting the results, including the values used to compute performance metrics (accuracy, sensitivity, specificity, F-measure), build figures (e.g., PCA plots, confusion matrices), and generate tables, are available in the original datasets and fully included in the supplementary materials submitted with this article. These datasets are distributed under open licenses allowing unrestricted use: CC0 (UCI) and Kaggle’s standard open data license. No additional ethical, privacy, or security concerns apply. Both datasets are openly accessible for academic and research purposes and do not contain any personally identifiable information. However, as the current study is based on third-party data, the authors were not involved in the original data collection process . To the best of our knowledge: • The UCI Heart Disease dataset was originally contributed by researchers from the Cleveland Clinic Foundation and is widely used in medical data mining research. Specific details regarding ethical approval and informed consent for this dataset were not provided in the original UCI repository documentation. • The Kaggle Cardiovascular Disease dataset was uploaded by the contributor Y. Suliana, who stated that the data was anonymized and collected during routine clinical practice. However, no specific name of the ethics committee, approval date, or consent procedure is disclosed in the dataset description. As per the policies of UCI and Kaggle, datasets are made publicly available under the assumption that all ethical requirements and informed consent procedures were handled appropriately by the original data custodians. Since no personally identifiable data is included, and the data is anonymized, no additional ethical approval or consent was required for our use of these datasets in accordance with our institutional guidelines and the Declaration of Helsinki. Acknowledgments This manuscript was prepared using AI-driven tools to guarantee academic honesty by citing the proper papers, increasing understanding by increasing linguistic clarity, and providing comprehensive literature analysis. Grammarly and Paperpal were used to examine the text for grammatical mistakes, typos, and punctuation errors. The comprehension power of Quillbot was used to put across complicated ideas concisely while maintaining the original context and meaning. Scopus AI and Consensus.app, both intuitive and intelligent search tools, helped us to understand and enrich our insights with unprecedented speed and clarity. Scholarcy helped improve the pace of the process as it abstracted related academic articles and critical findings, thereby helping bring together existing research which let to identifying research gaps. We employed Turnitin software to account for plagiarism check. References 1. Severino P, et al. : Ischemic Heart Disease Pathophysiology Paradigms Overview: From Plaque Activation to Microvascular Dysfunction. Int. J. Mol. Sci. Oct. 2020; 21 : 8118. PubMed Abstract | Publisher Full Text | Free Full Text 2. Cardiovascular diseases (CVDs). (accessed Mar. 16, 2023). Reference Source 3. Bolhasani H, Mohseni M, Rahmani AM: Deep learning applications for IoT in health care: A systematic review. Inform. Med. Unlocked. Jan. 2021; 23 : 100550. Publisher Full Text 4. Introduction to Recurrent Neural Network - GeeksforGeeks. (accessed Mar. 15, 2023). Reference Source 5. Gao R, Du L, Duru O, et al. : Time series forecasting based on echo state network and empirical wavelet transformation. Appl. Soft Comput. Apr. 2021; 102 : 107111. Publisher Full Text 6. Huang Z, et al. : Functional deep echo state network improved by a bi-level optimization approach for multivariate time series classification. Appl. Soft Comput. Jul. 2021; 106 : 107314. Publisher Full Text 7. Mhathesh TSR, Andrew J, Martin Sagayam K, et al. : A 3d convolutional neural network for bacterial image classification. Adv. Intell. Syst. Comput. Springer; 2021; pp. 419–431. Publisher Full Text 8. Wang F, et al. : Residual attention network for image classification. Proc. IEEE Conf. Comput. Vis. Pattern Recognit. 2017; 3156–3164. 9. Cenitta D, Arjunan RV, Prema KV: Ischemic Heart Disease Multiple Imputation Technique Using Machine Learning Algorithm. Eng. Sci. Sep. 2022; 19 : 262–272. Publisher Full Text 10. Li X, et al. : Automatic heartbeat classification using S-shaped reconstruction and a squeeze-and-excitation residual network. Elsevier, Comput. Biol. Med. 2022; 140 : 105108. PubMed Abstract | Publisher Full Text 11. Li B, Li Z, Yang Y: Residual attention graph convolutional network for web services classification. Neurocomputing. Jun. 2021; 440 : 45–57. Publisher Full Text 12. Sun X, Li T, Li Q, et al. : Deep belief echo-state network and its application to time series prediction. Knowl.-Based Syst. Aug. 2017; 130 : 17–29. Publisher Full Text 13. Zhang A, Zhu W, Li J: Spiking echo state convolutional neural network for robust time series classification. IEEE Access. 2019; 7 : 4927–4935. Publisher Full Text 14. Andrew J, Fiona R, Caleb Andrew H: Comparative study of various deep convolutional neural networks in the early prediction of cancer. 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019. Institute of Electrical and Electronics Engineers Inc.; May 2019; pp. 884–890. Publisher Full Text 15. Andrew Onesimu J, Karthikeyan J: An efficient privacy-preserving deep learning scheme for medical image analysis. Journal of Information Technology Management, vol. 12, no. Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications. Dec. 2021; 50–67. Publisher Full Text 16. Wang Q, Wang L, Liu Y, et al. : Time Series Prediction with Incomplete Dataset Based on Deep Bidirectional Echo State Network. IEEE Access. 2019; 7 : 152533–152544. Publisher Full Text 17. Ren W, Wang Y, Han M: Time series prediction based on echo state network tuned by divided adaptive multi-objective differential evolution algorithm. Soft. Comput. Mar. 2021; 25 (6): 4489–4502. Publisher Full Text 18. Doppala BP, Bhattacharyya D, Janarthanan M, et al. : A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques. J. Healthc. Eng. 2022; 2022 : 1–13. PubMed Abstract | Publisher Full Text | Free Full Text 19. Ampavathi A, Saradhi TV: Multi disease-prediction framework using hybrid deep learning: an optimal prediction model. Comput. Methods Biomech. Biomed. Engin. 2021; 24 (10): 1146–1168. PubMed Abstract | Publisher Full Text 20. Chandrasekaran ST, Banerjee I, Sanyal A: 7.5nJ/inference CMOS Echo State Network for Coronary Heart Disease prediction. ESSDERC 2021-IEEE 51st European Solid-State Device Research Conference (ESSDERC). Sep. 2021; pp. 103–106. Publisher Full Text 21. Liu Y, et al. : Automatic Detection of ECG Abnormalities by Using an Ensemble of Deep Residual Networks with Attention. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer; 2019; pp. 88–95. Publisher Full Text 22. Guo C, Zhang J, Liu Y, et al. : Recursion Enhanced Random Forest with an Improved Linear Model (RERF-ILM) for Heart Disease Detection on the Internet of Medical Things Platform. IEEE Access. 2020; 8 : 59247–59256. Publisher Full Text 23. Suresh T, Assegie TA, Rajkumar S, et al. : A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model. Int. J. Electr. Comput. Eng. 2022; 12 (2): 1831–1838. Publisher Full Text 24. Bhavekar GS, Das Goswami A: A hybrid model for heart disease prediction using recurrent neural network and long short term memory. Int. J. Inf. Technol. (Singapore). Jun. 2022; 14 (4): 1781–1789. Publisher Full Text 25. Sonawane R, Patil H: Automated heart disease prediction model by hybrid heuristic-based feature optimization and enhanced clustering. Biomed. Signal Process. Control. Feb. 2022; 72 : 103260. Publisher Full Text 26. Archana KS, Sivakumar B, Kuppusamy R, et al. : Automated Cardioailment Identification and Prevention by Hybrid Machine Learning Models. Comput. Math. Methods Med. 2022; 2022 : 1–8. PubMed Abstract | Publisher Full Text | Free Full Text 27. Kusuma S, Jothi KR: Heart disease classification using multiple K-PCA and hybrid deep learning approach. Comput. Syst. Sci. Eng. 2022; 41 (3): 1273–1289. Publisher Full Text 28. Nagavelli U, Samanta D, Chakraborty P: Machine Learning Technology-Based Heart Disease Detection Models. J. Healthc. Eng. 2022; 2022 : 1–9. PubMed Abstract | Publisher Full Text | Free Full Text 29. Sonawane R, Patil HD: Prediction of Heart Disease by Optimized Distance and Density-Based Clustering. Proceedings of the 2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022. Institute of Electrical and Electronics Engineers Inc; 2022; pp. 1001–1008. Publisher Full Text 30. Cardiovascular Disease dataset|Kaggle. (accessed Mar. 15, 2023). Reference Source 31. UCI Machine Learning Repository: Heart Disease Data Set. (accessed Mar. 15, 2023). Reference Source 32. Cenitta D, Vijaya Arjunan R, Prema KV: Ischemic Heart Disease Prediction Using Optimized Squirrel Search Feature Selection Algorithm. Publisher Full Text 33. Rani P, Kumar R, Ahmed NMOS, et al. : A decision support system for heart disease prediction based upon machine learning. J. Reliab. Intell. Environ. 2021; 7 : 263–275. Publisher Full Text 34. Jabbar MA, Deekshatulu BL, Chandra P: Prediction of heart disease using random forest and feature subset selection. Adv. Intell. Syst. Comput. 2016; 424 : 187–196. Publisher Full Text 35. Verma L, Mathur MK: Deep learning based model for decision support with case based reasoning. International Journal of Innovative Technology and Exploring Engineering. 2020; 8 (6C): 149–153. 36. Latha CBC, Jeeva SC: Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform. Med. Unlocked. Jan. 2019; 16 : 100203. Publisher Full Text 37. Tama BA, Im S, Lee S: Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble.2020. Publisher Full Text 38. Maiga J, Hungilo GG, et al. : Comparison of Machine Learning Models in Prediction of Cardiovascular Disease Using Health Record Data. International Conference on Informatics, Multimedia, Cyber and Information System. 2019; pp. 45–48. Publisher Full Text 39. Hagan R, Gillan CJ, Mallett F: Comparison of machine learning methods for the classification of cardiovascular disease. Inform. Med. Unlocked. Jan. 2021; 24 : 100606. Publisher Full Text 40. Bhoyar S, Wagholikar N, Bakshi K, et al. : Real-time Heart Disease Prediction System using Multilayer Perceptron; Real-time Heart Disease Prediction System using Multilayer Perceptron. International Conference for Emerging Technology. 2021. Publisher Full Text 41. Theerthagiri P, Vidya J: Cardiovascular disease prediction using recursive feature elimination and gradient boosting classification techniques. Expert. Syst. 2022; 39 . Publisher Full Text 42. Uddin MN, Halder RK: An ensemble method based multilayer dynamic system to predict cardiovascular disease using machine learning approach. Inform. Med. Unlocked. Jan. 2021; 24 : 100584. Publisher Full Text 43. Bharti R, Khamparia A, Shabaz M, et al. : Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Comput. Intell. Neurosci. 2021; 2021 . PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 Jul 2025 ADD YOUR COMMENT Comment Author details Author details 1 Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 2 Computer Science and Engineering, AJ Institute of Engineering and Technology, Mangalore, Karnataka, India Cenitta D Roles: Methodology, Project Administration VIijaya Arjunan Ranganathan Roles: Conceptualization, Writing – Review & Editing Tanuja Shailesh Roles: Writing – Review & Editing Andrew J Roles: Data Curation Arul N Roles: Visualization Praveen Pai T Roles: Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 16 Sep 2025, 14:650 https://doi.org/10.12688/f1000research.165575.2 version 1 Published: 03 Jul 2025, 14:650 https://doi.org/10.12688/f1000research.165575.1 Copyright © 2025 D C et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article D C, Ranganathan VA, Shailesh T et al. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.12688/f1000research.165575.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 03 Jul 2025 Views 0 Cite How to cite this report: MEMON MH. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r403945 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-403945 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Sep 2025 MUHAMMAD HAMMAD MEMON , Southwest University of Science and Technology, Sichuan, China Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.182263.r403945 Summary of the Article: The manuscript introduces a Hybrid Residual Attention with Echo State Network (HRAESN) model for ischemic heart disease (IHD) prediction. The model integrates Attention Residual Learning (ARL) to enhance feature extraction and Echo State ... Continue reading READ ALL Summary of the Article: The manuscript introduces a Hybrid Residual Attention with Echo State Network (HRAESN) model for ischemic heart disease (IHD) prediction. The model integrates Attention Residual Learning (ARL) to enhance feature extraction and Echo State Networks (ESN) for efficient time-series processing. Two datasets are used: the Kaggle Cardiovascular Disease dataset (70,000 samples) and the UCI Heart Disease dataset (303 samples). The authors report very high performance (up to 98.4% accuracy), claiming that HRAESN outperforms traditional ML/DL baselines. The study is relevant and well-motivated, with clear clinical importance. However, there are major concerns regarding methodological rigor, reproducibility, and statistical robustness . Major Concerns Presentation and Literature Coverage The manuscript is generally clear, but the literature review is overly descriptive and includes some weak references (e.g., tutorial websites). Prior work on combining attention and ESN (e.g., Deep Belief Echo-State Networks, Graph Residual Attention) is not sufficiently discussed. The novelty contribution must be better distinguished . Study Design and Technical Soundness The reported performance (>97% accuracy) is unrealistically high for these datasets and suggests possible overfitting or data leakage . Only a single 80:20 train-test split is reported. This is not sufficient for robust evaluation in medical ML. At minimum, k-fold cross-validation with stratified sampling is required. Methods and Replication Details of the Ischemic Heart Disease Multiple Imputation Technique are insufficient. The method is referenced but not described in reproducible detail. No code, model weights, or supplementary scripts are provided, making replication difficult. Statistical Analysis No statistical significance testing (e.g., McNemar’s test, paired t-test, Wilcoxon signed-rank test) is provided. Reported differences may not be statistically meaningful. Metrics such as ROC curves, AUC, calibration plots, and precision-recall curves should be included for clinical interpretability. Reproducibility and Source Data Although the datasets are public, the exact preprocessing steps and imputation pipeline are not fully transparent , which limits reproducibility. PCA plots and confusion matrices are shown but lack supporting raw numbers or code availability. Support for Conclusions While results are promising, conclusions about clinical utility are overstated . Without independent external validation on real hospital datasets, it is premature to suggest readiness for clinical deployment. Limitations such as dataset imbalance, computational cost, and lack of external validation are only briefly acknowledged and need stronger discussion. Minor Comments Some sections could be streamlined (particularly Related Works). Figures would benefit from statistical annotations (e.g., significance levels). The ethics statement should clarify whether the Kaggle dataset contributor had appropriate institutional approval. Writing is generally clear but could be more concise in parts. Recommendations to Improve the Manuscript Re-run experiments with 10-fold cross-validation and report mean ± standard deviation. Add statistical tests to confirm whether improvements over baselines are significant. Provide algorithmic details of the imputation method and release source code/models . Include AUC/ROC, calibration, and PR curves for stronger evaluation. Strengthen the novelty discussion by differentiating HRAESN from earlier ESN+attention studies. Expand the limitations section , especially regarding generalizability and clinical applicability. Final Recommendation Major Revision The study addresses an important healthcare challenge and proposes an interesting hybrid deep learning approach. However, methodological rigor, reproducibility, and statistical analysis must be improved to make the findings scientifically sound and credible for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Artificial Intelligence and Machine Learning, Medical Data Mining and Predictive Analytics, Deep Learning for Healthcare Applications, Network Security and Cloud Computing. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT MEMON MH. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r403945 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-403945 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: We thank Reviewer for the detailed assessment. The reviewer highlighted the clinical relevance of our work while raising concerns about methodology, reproducibility, and statistical robustness. We carefully revised the manuscript to address all points raised. Below we provide a structured response. Reviewer#3, Concern # 2: Presentation and Literature Coverage Author response: We acknowledge this important observation. The Related Works section has been streamlined and focused on high-quality peer-reviewed studies. We expanded discussion of prior attention+ESN combinations, including Deep Belief Echo-State Networks (DBEN) and Graph Residual Attention models, to clearly distinguish our contribution. Our novelty lies in extending ESNs beyond time-series into structured clinical tabular data, integrated with ARL and combined with a tailored imputation framework. Author action: Revised Section 2 (Related Works) to be more concise, replaced weak/tutorial references with peer-reviewed sources, and explicitly clarified novelty. Reviewer#3, Concern # 3: Study Design and Technical Soundness Author response: We appreciate this concern. To strengthen robustness, we re-ran experiments with 5-fold and 10-fold stratified cross-validation in addition to the 80:20 split. Results are now reported as mean ± standard deviation. Performance remained consistently high, though slightly lower than single-split values, confirming stability without evidence of leakage. Author action: Added cross-validation experiments and updated Tables 7–10 with mean ± SD. Reproducibility pipeline clarified in Section 3.5 Methodology. Reviewer#3, Concern # 4: Methods and Replication Author response: We agree. The Ischemic Heart Disease Multiple Imputation Technique (IHD-MIT) is now described in step-by-step detail (predictor selection, iterative regression, variance preservation). For transparency, we have expanded the methodological description of the IHD-MIT imputation pipeline and model implementation in detail. Author action: Expanded Section 3.5.1 (IHD-MIT) with algorithmic details. Reviewer#3, Concern # 5: Statistical Analysis Author response: We fully agree. We added statistical significance testing (McNemar’s test for paired predictions, Wilcoxon signed-rank across folds) to confirm differences. Additionally, we now report ROC curves, AUC values, and calibration plots for clinical interpretability. Results demonstrate that HRAESN improvements are statistically significant (p < 0.05). Author action: Added Figure 7 for ROC/AUC; included calibration analysis. Expanded Results Section 4.2–4.3 to include statistical testing. Reviewer#3, Concern # 6: Reproducibility and Source Data Author response: We clarified all preprocessing steps, including normalization, imputation, train-test stratification, and cross-validation. Author action: Updated Figures 3–6 captions with supporting details. Reviewer#3, Concern # 7: Support for Conclusions Author response: We agree and have moderated claims. We now clearly state that this work is a proof-of-concept and not clinically deployable yet. We expanded Limitations to address external validation needs, potential bias from imputation, dataset imbalance, computational cost, and the need for interpretability studies. Author action: Expanded Section 6 Limitations and Future Directions, emphasizing generalizability and next steps toward real-world validation. Reviewer#3, Concern # 8: Minor Comments Author response: We thank the reviewer. Related Works was condensed (as above). Figures now include statistical annotations (significance levels). We clarified that Kaggle data are anonymized and released under open license, with ethical approvals obtained by original curators. The manuscript was carefully edited for conciseness. Author action: Revised Section 2, updated figure annotations, clarified Ethics Statement, and streamlined prose throughout. Reviewer#3, Concern # 1: Reviewer Recommendations Implemented Author response: Re-ran experiments with 10-fold cross-validation. Reported mean ± SD for all metrics. Added statistical tests (McNemar, Wilcoxon). Included ROC curves. Provided algorithmic details of IHD-MIT. Strengthened novelty discussion and limitations. · We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: We thank Reviewer for the detailed assessment. The reviewer highlighted the clinical relevance of our work while raising concerns about methodology, reproducibility, and statistical robustness. We carefully revised the manuscript to address all points raised. Below we provide a structured response. Reviewer#3, Concern # 2: Presentation and Literature Coverage Author response: We acknowledge this important observation. The Related Works section has been streamlined and focused on high-quality peer-reviewed studies. We expanded discussion of prior attention+ESN combinations, including Deep Belief Echo-State Networks (DBEN) and Graph Residual Attention models, to clearly distinguish our contribution. Our novelty lies in extending ESNs beyond time-series into structured clinical tabular data, integrated with ARL and combined with a tailored imputation framework. Author action: Revised Section 2 (Related Works) to be more concise, replaced weak/tutorial references with peer-reviewed sources, and explicitly clarified novelty. Reviewer#3, Concern # 3: Study Design and Technical Soundness Author response: We appreciate this concern. To strengthen robustness, we re-ran experiments with 5-fold and 10-fold stratified cross-validation in addition to the 80:20 split. Results are now reported as mean ± standard deviation. Performance remained consistently high, though slightly lower than single-split values, confirming stability without evidence of leakage. Author action: Added cross-validation experiments and updated Tables 7–10 with mean ± SD. Reproducibility pipeline clarified in Section 3.5 Methodology. Reviewer#3, Concern # 4: Methods and Replication Author response: We agree. The Ischemic Heart Disease Multiple Imputation Technique (IHD-MIT) is now described in step-by-step detail (predictor selection, iterative regression, variance preservation). For transparency, we have expanded the methodological description of the IHD-MIT imputation pipeline and model implementation in detail. Author action: Expanded Section 3.5.1 (IHD-MIT) with algorithmic details. Reviewer#3, Concern # 5: Statistical Analysis Author response: We fully agree. We added statistical significance testing (McNemar’s test for paired predictions, Wilcoxon signed-rank across folds) to confirm differences. Additionally, we now report ROC curves, AUC values, and calibration plots for clinical interpretability. Results demonstrate that HRAESN improvements are statistically significant (p < 0.05). Author action: Added Figure 7 for ROC/AUC; included calibration analysis. Expanded Results Section 4.2–4.3 to include statistical testing. Reviewer#3, Concern # 6: Reproducibility and Source Data Author response: We clarified all preprocessing steps, including normalization, imputation, train-test stratification, and cross-validation. Author action: Updated Figures 3–6 captions with supporting details. Reviewer#3, Concern # 7: Support for Conclusions Author response: We agree and have moderated claims. We now clearly state that this work is a proof-of-concept and not clinically deployable yet. We expanded Limitations to address external validation needs, potential bias from imputation, dataset imbalance, computational cost, and the need for interpretability studies. Author action: Expanded Section 6 Limitations and Future Directions, emphasizing generalizability and next steps toward real-world validation. Reviewer#3, Concern # 8: Minor Comments Author response: We thank the reviewer. Related Works was condensed (as above). Figures now include statistical annotations (significance levels). We clarified that Kaggle data are anonymized and released under open license, with ethical approvals obtained by original curators. The manuscript was carefully edited for conciseness. Author action: Revised Section 2, updated figure annotations, clarified Ethics Statement, and streamlined prose throughout. Reviewer#3, Concern # 1: Reviewer Recommendations Implemented Author response: Re-ran experiments with 10-fold cross-validation. Reported mean ± SD for all metrics. Added statistical tests (McNemar, Wilcoxon). Included ROC curves. Provided algorithmic details of IHD-MIT. Strengthened novelty discussion and limitations. · We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: We thank Reviewer for the detailed assessment. The reviewer highlighted the clinical relevance of our work while raising concerns about methodology, reproducibility, and statistical robustness. We carefully revised the manuscript to address all points raised. Below we provide a structured response. Reviewer#3, Concern # 2: Presentation and Literature Coverage Author response: We acknowledge this important observation. The Related Works section has been streamlined and focused on high-quality peer-reviewed studies. We expanded discussion of prior attention+ESN combinations, including Deep Belief Echo-State Networks (DBEN) and Graph Residual Attention models, to clearly distinguish our contribution. Our novelty lies in extending ESNs beyond time-series into structured clinical tabular data, integrated with ARL and combined with a tailored imputation framework. Author action: Revised Section 2 (Related Works) to be more concise, replaced weak/tutorial references with peer-reviewed sources, and explicitly clarified novelty. Reviewer#3, Concern # 3: Study Design and Technical Soundness Author response: We appreciate this concern. To strengthen robustness, we re-ran experiments with 5-fold and 10-fold stratified cross-validation in addition to the 80:20 split. Results are now reported as mean ± standard deviation. Performance remained consistently high, though slightly lower than single-split values, confirming stability without evidence of leakage. Author action: Added cross-validation experiments and updated Tables 7–10 with mean ± SD. Reproducibility pipeline clarified in Section 3.5 Methodology. Reviewer#3, Concern # 4: Methods and Replication Author response: We agree. The Ischemic Heart Disease Multiple Imputation Technique (IHD-MIT) is now described in step-by-step detail (predictor selection, iterative regression, variance preservation). For transparency, we have expanded the methodological description of the IHD-MIT imputation pipeline and model implementation in detail. Author action: Expanded Section 3.5.1 (IHD-MIT) with algorithmic details. Reviewer#3, Concern # 5: Statistical Analysis Author response: We fully agree. We added statistical significance testing (McNemar’s test for paired predictions, Wilcoxon signed-rank across folds) to confirm differences. Additionally, we now report ROC curves, AUC values, and calibration plots for clinical interpretability. Results demonstrate that HRAESN improvements are statistically significant (p < 0.05). Author action: Added Figure 7 for ROC/AUC; included calibration analysis. Expanded Results Section 4.2–4.3 to include statistical testing. Reviewer#3, Concern # 6: Reproducibility and Source Data Author response: We clarified all preprocessing steps, including normalization, imputation, train-test stratification, and cross-validation. Author action: Updated Figures 3–6 captions with supporting details. Reviewer#3, Concern # 7: Support for Conclusions Author response: We agree and have moderated claims. We now clearly state that this work is a proof-of-concept and not clinically deployable yet. We expanded Limitations to address external validation needs, potential bias from imputation, dataset imbalance, computational cost, and the need for interpretability studies. Author action: Expanded Section 6 Limitations and Future Directions, emphasizing generalizability and next steps toward real-world validation. Reviewer#3, Concern # 8: Minor Comments Author response: We thank the reviewer. Related Works was condensed (as above). Figures now include statistical annotations (significance levels). We clarified that Kaggle data are anonymized and released under open license, with ethical approvals obtained by original curators. The manuscript was carefully edited for conciseness. Author action: Revised Section 2, updated figure annotations, clarified Ethics Statement, and streamlined prose throughout. Reviewer#3, Concern # 1: Reviewer Recommendations Implemented Author response: Re-ran experiments with 10-fold cross-validation. Reported mean ± SD for all metrics. Added statistical tests (McNemar, Wilcoxon). Included ROC curves. Provided algorithmic details of IHD-MIT. Strengthened novelty discussion and limitations. · We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: We thank Reviewer for the detailed assessment. The reviewer highlighted the clinical relevance of our work while raising concerns about methodology, reproducibility, and statistical robustness. We carefully revised the manuscript to address all points raised. Below we provide a structured response. Reviewer#3, Concern # 2: Presentation and Literature Coverage Author response: We acknowledge this important observation. The Related Works section has been streamlined and focused on high-quality peer-reviewed studies. We expanded discussion of prior attention+ESN combinations, including Deep Belief Echo-State Networks (DBEN) and Graph Residual Attention models, to clearly distinguish our contribution. Our novelty lies in extending ESNs beyond time-series into structured clinical tabular data, integrated with ARL and combined with a tailored imputation framework. Author action: Revised Section 2 (Related Works) to be more concise, replaced weak/tutorial references with peer-reviewed sources, and explicitly clarified novelty. Reviewer#3, Concern # 3: Study Design and Technical Soundness Author response: We appreciate this concern. To strengthen robustness, we re-ran experiments with 5-fold and 10-fold stratified cross-validation in addition to the 80:20 split. Results are now reported as mean ± standard deviation. Performance remained consistently high, though slightly lower than single-split values, confirming stability without evidence of leakage. Author action: Added cross-validation experiments and updated Tables 7–10 with mean ± SD. Reproducibility pipeline clarified in Section 3.5 Methodology. Reviewer#3, Concern # 4: Methods and Replication Author response: We agree. The Ischemic Heart Disease Multiple Imputation Technique (IHD-MIT) is now described in step-by-step detail (predictor selection, iterative regression, variance preservation). For transparency, we have expanded the methodological description of the IHD-MIT imputation pipeline and model implementation in detail. Author action: Expanded Section 3.5.1 (IHD-MIT) with algorithmic details. Reviewer#3, Concern # 5: Statistical Analysis Author response: We fully agree. We added statistical significance testing (McNemar’s test for paired predictions, Wilcoxon signed-rank across folds) to confirm differences. Additionally, we now report ROC curves, AUC values, and calibration plots for clinical interpretability. Results demonstrate that HRAESN improvements are statistically significant (p < 0.05). Author action: Added Figure 7 for ROC/AUC; included calibration analysis. Expanded Results Section 4.2–4.3 to include statistical testing. Reviewer#3, Concern # 6: Reproducibility and Source Data Author response: We clarified all preprocessing steps, including normalization, imputation, train-test stratification, and cross-validation. Author action: Updated Figures 3–6 captions with supporting details. Reviewer#3, Concern # 7: Support for Conclusions Author response: We agree and have moderated claims. We now clearly state that this work is a proof-of-concept and not clinically deployable yet. We expanded Limitations to address external validation needs, potential bias from imputation, dataset imbalance, computational cost, and the need for interpretability studies. Author action: Expanded Section 6 Limitations and Future Directions, emphasizing generalizability and next steps toward real-world validation. Reviewer#3, Concern # 8: Minor Comments Author response: We thank the reviewer. Related Works was condensed (as above). Figures now include statistical annotations (significance levels). We clarified that Kaggle data are anonymized and released under open license, with ethical approvals obtained by original curators. The manuscript was carefully edited for conciseness. Author action: Revised Section 2, updated figure annotations, clarified Ethics Statement, and streamlined prose throughout. Reviewer#3, Concern # 1: Reviewer Recommendations Implemented Author response: Re-ran experiments with 10-fold cross-validation. Reported mean ± SD for all metrics. Added statistical tests (McNemar, Wilcoxon). Included ROC curves. Provided algorithmic details of IHD-MIT. Strengthened novelty discussion and limitations. · We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Shrestha D. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r406539 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-406539 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 04 Sep 2025 Dhadkan Shrestha , Texas State University College of Science and Engineering, San Marcos, Texas, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.182263.r406539 1. Summary of the Article The manuscript presents a Hybrid Residual Attention with Echo State Network (HRAESN) model for predicting ischemic heart disease (IHD). The approach integrates Attention Residual Learning (ARL) for feature extraction with Echo State Networks (ESNs) ... Continue reading READ ALL 1. Summary of the Article The manuscript presents a Hybrid Residual Attention with Echo State Network (HRAESN) model for predicting ischemic heart disease (IHD). The approach integrates Attention Residual Learning (ARL) for feature extraction with Echo State Networks (ESNs) for efficient time-series learning. The study evaluates performance on two publicly available datasets: the Kaggle Cardiovascular Disease dataset (70,000 records) and the UCI Heart Disease dataset (303 records) . Missing values were handled using a tailored Multiple Imputation Technique . The proposed model achieved high classification performance, with accuracies of 98.4% (Kaggle) and 97.7% (UCI) , surpassing traditional ML and DL baselines. The authors conclude that the model offers strong potential as a clinical decision-support tool for early IHD detection. 2. Evaluation of Key Criteria (a) Clarity, Accuracy, and Literature Coverage Assessment : Yes (with minor improvements suggested) The manuscript is clearly written, structured logically, and cites a broad range of recent literature. The background is thorough and informative. A few parts (e.g., the objectives and problem statement) overlap slightly and could be streamlined for conciseness. Constructive suggestions : Condense repetitive sections to make the narrative flow smoother. More explicitly highlight how this approach differs from other recent hybrid deep learning works to strengthen the novelty claim. (b) Study Design and Technical Soundness Assessment : Yes The study design is technically sound, and the proposed model is innovative. The integration of ARL and ESN is well motivated. The results are very strong, though the extremely high accuracy on the small UCI dataset raises the possibility of overfitting. Still, the use of dropout and a robust hyperparameter setup is a positive point. Suggestions : For added robustness, apply k-fold cross-validation (especially for UCI dataset). Briefly discuss class balance and whether any balancing strategy (e.g., weighting) was needed. (c) Methods and Replicability Assessment : Partly The mathematical formulation is clear, and hyperparameters are well documented. This is very helpful. However, replication would be easier if code or pseudo-code for preprocessing and training were made available. Suggestions : Consider providing code, pseudocode, or a detailed pipeline in supplementary materials. Clarify how hyperparameters were tuned (manual search, grid search, etc.). (d) Statistical Analysis and Interpretation Assessment : Yes The authors present a comprehensive set of performance metrics (accuracy, sensitivity, specificity, F1, Kappa, FAR/FRR), which is commendable. Interpretation is generally appropriate. One minor limitation is the absence of variance/confidence intervals across multiple runs. Suggestions : Indicate whether results are from a single run or averaged across runs. If possible, include confidence intervals or standard deviations. (e) Availability of Source Data Assessment : Yes The datasets (UCI and Kaggle) are publicly available and properly cited. Ethical considerations are addressed. This ensures reproducibility of the raw data. Suggestions : It would be helpful to share the preprocessed datasets or preprocessing scripts used before training. (f) Conclusions and Support from Results Assessment : Yes The conclusions are well supported by the reported results. The performance improvement over baselines is clear. That said, claims about clinical applicability should be framed as potential future applications rather than immediate readiness. Suggestions : Add a brief “Limitations” section noting that real-world hospital validation is pending. Slightly temper statements on clinical deployment to emphasize this is a proof-of-concept 3. Key Points to Address To make the manuscript even stronger, the authors should consider: Adding cross-validation results (especially for the UCI dataset). Reporting variance or confidence intervals for performance metrics. Providing code/pseudocode or preprocessing details for easier replication. Including a short limitations section (dataset size, clinical validation, computational cost). Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine Learning, Artificial Intelligence, Big Data I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Shrestha D. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r406539 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-406539 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: We thank the reviewer for the accurate and concise summary of our work. We appreciate the recognition of our proposed Hybrid Residual Attention with Echo State Network (HRAESN) model, our methodological contributions (Attention Residual Learning combined with Echo State Networks), and our evaluation using the Kaggle and UCI datasets. We also thank the reviewer for noting our strategy for handling missing values and the strong performance achieved by the model. Reviewer#2, Concern # 2: Clarity, Accuracy, and Literature Coverage Author response: We agree with this suggestion. We revised the Introduction to remove overlap between the problem statement and objectives, improving narrative flow. Additionally, we added a new paragraph at the end of the Introduction to explicitly highlight novelty: (i) integration of ARL with ESNs, (ii) extending ESNs to structured/tabular clinical data, and (iii) introducing an IHD-specific multiple imputation method. Author action: Revised Introduction: merged problem statement + objectives into a concise paragraph. Added final paragraph in Introduction to emphasize novelty. Reviewer#2, Concern # 3: Study Design and Technical Soundness Author response: We thank the reviewer for this important point. To address robustness, we added text in Methods clarifying that k-fold cross-validation (k=5) was performed on the UCI dataset, confirming stable results across folds. We also report class balance: UCI dataset (~54% IHD, ~46% healthy) and Kaggle dataset (~50% each), showing no major imbalance. No resampling or weighting was needed. We further acknowledge the potential risk of overfitting in the Discussion as a limitation. Author action: Added in Section 3.5: description of k-fold cross-validation on UCI dataset. Added in Section 3.1.4: class balance description. Expanded Discussion: limitation noting overfitting risk in small datasets. Reviewer#2, Concern # 4: Methods and Replicability Author response: We appreciate this suggestion. To enhance replicability, we included a pseudo-code style Algorithm (Algorithm 1) in the Methods section, summarizing the preprocessing, model training, and evaluation pipeline. We also clarified that hyperparameters were tuned via grid search, selecting the configuration with the highest validation F1-score. Author action: Added Algorithm 1 (pipeline) in Section 3.5. Clarified hyperparameter tuning strategy (grid search). Reviewer#2, Concern # 5: Statistical Analysis and Interpretation Author response: We agree. Results now explicitly state they are averaged across multiple runs. We also computed 95% confidence intervals for all primary metrics using bootstrap resampling (1000 iterations). Author action: Updated Results to note averaged results across runs. Reviewer#2, Concern # 6: Availability of Source Data Author response: We thank the reviewer for this comment. While raw datasets are already public, we recognize that preprocessing adds value for replication. We now provide a detailed preprocessing description in Methods (Section 3.1.4) and make scripts available upon request. Author action: Expanded Section 3.1.4 with detailed preprocessing description. Reviewer#2, Concern # 7: Conclusions and Support from Results Author response: We agree with this suggestion. The Discussion has been expanded with a new Limitations subsection addressing dataset size, lack of external clinical validation, potential imputation bias, and computational cost. Statements on clinical application have been revised to emphasize that this is a proof-of-concept with potential future clinical use. Author action: Expanded Discussion with Limitations subsection. Rephrased Conclusion to emphasize proof-of-concept, not immediate deployment. Reviewer#2, Concern # 8: Key Points to Address Author response: All these points have been addressed in the revision: Cross-validation results for UCI dataset included. 95% confidence intervals. Algorithm 1 (pseudo-code pipeline) added in Methods. Expanded Discussion with a Limitations section. Author action: Revisions made in Sections 3.1.4, 3.5, Results, and Discussion. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: We thank the reviewer for the accurate and concise summary of our work. We appreciate the recognition of our proposed Hybrid Residual Attention with Echo State Network (HRAESN) model, our methodological contributions (Attention Residual Learning combined with Echo State Networks), and our evaluation using the Kaggle and UCI datasets. We also thank the reviewer for noting our strategy for handling missing values and the strong performance achieved by the model. Reviewer#2, Concern # 2: Clarity, Accuracy, and Literature Coverage Author response: We agree with this suggestion. We revised the Introduction to remove overlap between the problem statement and objectives, improving narrative flow. Additionally, we added a new paragraph at the end of the Introduction to explicitly highlight novelty: (i) integration of ARL with ESNs, (ii) extending ESNs to structured/tabular clinical data, and (iii) introducing an IHD-specific multiple imputation method. Author action: Revised Introduction: merged problem statement + objectives into a concise paragraph. Added final paragraph in Introduction to emphasize novelty. Reviewer#2, Concern # 3: Study Design and Technical Soundness Author response: We thank the reviewer for this important point. To address robustness, we added text in Methods clarifying that k-fold cross-validation (k=5) was performed on the UCI dataset, confirming stable results across folds. We also report class balance: UCI dataset (~54% IHD, ~46% healthy) and Kaggle dataset (~50% each), showing no major imbalance. No resampling or weighting was needed. We further acknowledge the potential risk of overfitting in the Discussion as a limitation. Author action: Added in Section 3.5: description of k-fold cross-validation on UCI dataset. Added in Section 3.1.4: class balance description. Expanded Discussion: limitation noting overfitting risk in small datasets. Reviewer#2, Concern # 4: Methods and Replicability Author response: We appreciate this suggestion. To enhance replicability, we included a pseudo-code style Algorithm (Algorithm 1) in the Methods section, summarizing the preprocessing, model training, and evaluation pipeline. We also clarified that hyperparameters were tuned via grid search, selecting the configuration with the highest validation F1-score. Author action: Added Algorithm 1 (pipeline) in Section 3.5. Clarified hyperparameter tuning strategy (grid search). Reviewer#2, Concern # 5: Statistical Analysis and Interpretation Author response: We agree. Results now explicitly state they are averaged across multiple runs. We also computed 95% confidence intervals for all primary metrics using bootstrap resampling (1000 iterations). Author action: Updated Results to note averaged results across runs. Reviewer#2, Concern # 6: Availability of Source Data Author response: We thank the reviewer for this comment. While raw datasets are already public, we recognize that preprocessing adds value for replication. We now provide a detailed preprocessing description in Methods (Section 3.1.4) and make scripts available upon request. Author action: Expanded Section 3.1.4 with detailed preprocessing description. Reviewer#2, Concern # 7: Conclusions and Support from Results Author response: We agree with this suggestion. The Discussion has been expanded with a new Limitations subsection addressing dataset size, lack of external clinical validation, potential imputation bias, and computational cost. Statements on clinical application have been revised to emphasize that this is a proof-of-concept with potential future clinical use. Author action: Expanded Discussion with Limitations subsection. Rephrased Conclusion to emphasize proof-of-concept, not immediate deployment. Reviewer#2, Concern # 8: Key Points to Address Author response: All these points have been addressed in the revision: Cross-validation results for UCI dataset included. 95% confidence intervals. Algorithm 1 (pseudo-code pipeline) added in Methods. Expanded Discussion with a Limitations section. Author action: Revisions made in Sections 3.1.4, 3.5, Results, and Discussion. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: We thank the reviewer for the accurate and concise summary of our work. We appreciate the recognition of our proposed Hybrid Residual Attention with Echo State Network (HRAESN) model, our methodological contributions (Attention Residual Learning combined with Echo State Networks), and our evaluation using the Kaggle and UCI datasets. We also thank the reviewer for noting our strategy for handling missing values and the strong performance achieved by the model. Reviewer#2, Concern # 2: Clarity, Accuracy, and Literature Coverage Author response: We agree with this suggestion. We revised the Introduction to remove overlap between the problem statement and objectives, improving narrative flow. Additionally, we added a new paragraph at the end of the Introduction to explicitly highlight novelty: (i) integration of ARL with ESNs, (ii) extending ESNs to structured/tabular clinical data, and (iii) introducing an IHD-specific multiple imputation method. Author action: Revised Introduction: merged problem statement + objectives into a concise paragraph. Added final paragraph in Introduction to emphasize novelty. Reviewer#2, Concern # 3: Study Design and Technical Soundness Author response: We thank the reviewer for this important point. To address robustness, we added text in Methods clarifying that k-fold cross-validation (k=5) was performed on the UCI dataset, confirming stable results across folds. We also report class balance: UCI dataset (~54% IHD, ~46% healthy) and Kaggle dataset (~50% each), showing no major imbalance. No resampling or weighting was needed. We further acknowledge the potential risk of overfitting in the Discussion as a limitation. Author action: Added in Section 3.5: description of k-fold cross-validation on UCI dataset. Added in Section 3.1.4: class balance description. Expanded Discussion: limitation noting overfitting risk in small datasets. Reviewer#2, Concern # 4: Methods and Replicability Author response: We appreciate this suggestion. To enhance replicability, we included a pseudo-code style Algorithm (Algorithm 1) in the Methods section, summarizing the preprocessing, model training, and evaluation pipeline. We also clarified that hyperparameters were tuned via grid search, selecting the configuration with the highest validation F1-score. Author action: Added Algorithm 1 (pipeline) in Section 3.5. Clarified hyperparameter tuning strategy (grid search). Reviewer#2, Concern # 5: Statistical Analysis and Interpretation Author response: We agree. Results now explicitly state they are averaged across multiple runs. We also computed 95% confidence intervals for all primary metrics using bootstrap resampling (1000 iterations). Author action: Updated Results to note averaged results across runs. Reviewer#2, Concern # 6: Availability of Source Data Author response: We thank the reviewer for this comment. While raw datasets are already public, we recognize that preprocessing adds value for replication. We now provide a detailed preprocessing description in Methods (Section 3.1.4) and make scripts available upon request. Author action: Expanded Section 3.1.4 with detailed preprocessing description. Reviewer#2, Concern # 7: Conclusions and Support from Results Author response: We agree with this suggestion. The Discussion has been expanded with a new Limitations subsection addressing dataset size, lack of external clinical validation, potential imputation bias, and computational cost. Statements on clinical application have been revised to emphasize that this is a proof-of-concept with potential future clinical use. Author action: Expanded Discussion with Limitations subsection. Rephrased Conclusion to emphasize proof-of-concept, not immediate deployment. Reviewer#2, Concern # 8: Key Points to Address Author response: All these points have been addressed in the revision: Cross-validation results for UCI dataset included. 95% confidence intervals. Algorithm 1 (pseudo-code pipeline) added in Methods. Expanded Discussion with a Limitations section. Author action: Revisions made in Sections 3.1.4, 3.5, Results, and Discussion. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: We thank the reviewer for the accurate and concise summary of our work. We appreciate the recognition of our proposed Hybrid Residual Attention with Echo State Network (HRAESN) model, our methodological contributions (Attention Residual Learning combined with Echo State Networks), and our evaluation using the Kaggle and UCI datasets. We also thank the reviewer for noting our strategy for handling missing values and the strong performance achieved by the model. Reviewer#2, Concern # 2: Clarity, Accuracy, and Literature Coverage Author response: We agree with this suggestion. We revised the Introduction to remove overlap between the problem statement and objectives, improving narrative flow. Additionally, we added a new paragraph at the end of the Introduction to explicitly highlight novelty: (i) integration of ARL with ESNs, (ii) extending ESNs to structured/tabular clinical data, and (iii) introducing an IHD-specific multiple imputation method. Author action: Revised Introduction: merged problem statement + objectives into a concise paragraph. Added final paragraph in Introduction to emphasize novelty. Reviewer#2, Concern # 3: Study Design and Technical Soundness Author response: We thank the reviewer for this important point. To address robustness, we added text in Methods clarifying that k-fold cross-validation (k=5) was performed on the UCI dataset, confirming stable results across folds. We also report class balance: UCI dataset (~54% IHD, ~46% healthy) and Kaggle dataset (~50% each), showing no major imbalance. No resampling or weighting was needed. We further acknowledge the potential risk of overfitting in the Discussion as a limitation. Author action: Added in Section 3.5: description of k-fold cross-validation on UCI dataset. Added in Section 3.1.4: class balance description. Expanded Discussion: limitation noting overfitting risk in small datasets. Reviewer#2, Concern # 4: Methods and Replicability Author response: We appreciate this suggestion. To enhance replicability, we included a pseudo-code style Algorithm (Algorithm 1) in the Methods section, summarizing the preprocessing, model training, and evaluation pipeline. We also clarified that hyperparameters were tuned via grid search, selecting the configuration with the highest validation F1-score. Author action: Added Algorithm 1 (pipeline) in Section 3.5. Clarified hyperparameter tuning strategy (grid search). Reviewer#2, Concern # 5: Statistical Analysis and Interpretation Author response: We agree. Results now explicitly state they are averaged across multiple runs. We also computed 95% confidence intervals for all primary metrics using bootstrap resampling (1000 iterations). Author action: Updated Results to note averaged results across runs. Reviewer#2, Concern # 6: Availability of Source Data Author response: We thank the reviewer for this comment. While raw datasets are already public, we recognize that preprocessing adds value for replication. We now provide a detailed preprocessing description in Methods (Section 3.1.4) and make scripts available upon request. Author action: Expanded Section 3.1.4 with detailed preprocessing description. Reviewer#2, Concern # 7: Conclusions and Support from Results Author response: We agree with this suggestion. The Discussion has been expanded with a new Limitations subsection addressing dataset size, lack of external clinical validation, potential imputation bias, and computational cost. Statements on clinical application have been revised to emphasize that this is a proof-of-concept with potential future clinical use. Author action: Expanded Discussion with Limitations subsection. Rephrased Conclusion to emphasize proof-of-concept, not immediate deployment. Reviewer#2, Concern # 8: Key Points to Address Author response: All these points have been addressed in the revision: Cross-validation results for UCI dataset included. 95% confidence intervals. Algorithm 1 (pseudo-code pipeline) added in Methods. Expanded Discussion with a Limitations section. Author action: Revisions made in Sections 3.1.4, 3.5, Results, and Discussion. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Haue AD. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r401540 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-401540 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 25 Aug 2025 Amalie Dahl Haue , University of Copenhagen, Copenhagen, Denmark Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.182263.r401540 The research article by Ranganathan et al. presents a deep learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction derived from analysis of the Kaggle Cardiovascular Disease dataset and the UCI Heart Disease dataset. Their ... Continue reading READ ALL The research article by Ranganathan et al. presents a deep learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction derived from analysis of the Kaggle Cardiovascular Disease dataset and the UCI Heart Disease dataset. Their model (HRAEN) demonstrates superior perfomance with accuracy rating between 97.7% and 98.4%. Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Related works This section would benefit greatly from a more condensed presentation of the literature. Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Cardiology resident I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Haue AD. Reviewer Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r401540 ) The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-401540 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Author response: We thank the reviewer for this valuable suggestion. We have revised the introductory paragraph to better reflect current clinical practices, replacing outdated references (stress test, Holter monitoring) with contemporary modalities such as cardiac CT, RbPET, and coronary angiography. Author action: The Introduction now begins with a discussion of ischemic heart disease pathophysiology and updated diagnostic modalities. Reviewer#1, Concern # 2: Related works This section would benefit greatly from a more condensed presentation of the literature. Author response: We appreciate this suggestion. We revised the Related Works section to streamline the narrative, grouping studies under thematic categories (traditional ML, deep learning, hybrid models, ESN-based, and attention-based methods). Author action: Section 2 was restructured for conciseness while retaining comprehensiveness. Reviewer#1, Concern # 3: Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Author response: We acknowledge this concern. We now clearly define the target variables in both datasets: UCI: angiography-based “num” variable, binarized (0 = absence, 1–4 = presence of disease). Kaggle: “cardio” variable defined by combined clinical assessments (blood pressure, cholesterol, ECG). Author action: Added Section 3.1.4 Definition of Heart Disease in the Datasets. Reviewer#1, Concern # 4: Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? Author response: We appreciate this important comment. We have clarified in Section 3.1.4 how IHD was defined in each dataset (Kaggle: cardio; UCI: num attribute binarized). Missing data handling using the IHD Multiple Imputation Technique is now described. We also added clarification on how Echo State Networks were applied to structured tabular data (not time-series). Figures 1 and 2 were redesigned to show dataset composition, preprocessing, and architecture in greater detail. Author action: Section 3.1.4 updated with disease definition, missingness handling, and ESN applicability. Redesigned Figure 1 (workflow with dataset size, missing values, preprocessing, labels, metrics). Redesigned Figure 2 (detailed HRAESN architecture with ESN + ARL modules). Reviewer#1, Concern # 5: It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Author response: We agree this required clarification. While raw ECG series were not used, we adapted ESNs by treating patient feature vectors as structured sequences, mapping them into reservoir states to capture nonlinear feature dependencies. Author action: Added explanation in Section 3.5 Methodology – Application of ESNs to Tabular Data. Reviewer#1, Concern # 6: Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? Author response: We standardized terminology throughout: Class 0 = no IHD, Class 1 = IHD present. Author action: Updated class definitions consistently across Materials & Methods, Results, and figures. Reviewer#1, Concern # 7: New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Author response: We thank the reviewer. These metrics are now introduced in Evaluation Metrics subsection of Materials and Methods. Author action: Section 3.5 includes definitions of Kappa coefficient and Jaccard index. Reviewer#1, Concern # 8: Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? Author response: We have clarified the figure captions to indicate that Figure 4 reports performance metrics separately for both UCI and Kaggle datasets. Author action: Updated Figure 4 caption as suggested Reviewer#1, Concern # 9: For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Author response: We have now reported 95% confidence intervals using bootstrap resampling (1000 iterations) for all major performance metrics. Author action: Confidence intervals are included in tables as suggested. Reviewer#1, Concern # 10: Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Author response: We agree this was ambiguous. Figure 6 is intended to illustrate HRAESN error rates across datasets, while comparative results with baselines are in Tables 8–9. Author action: Figure 6 caption updated to clarify scope Reviewer#1, Concern # 11: Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Author response: We now provide a table of baseline characteristics (age, sex, cholesterol, blood pressure) for training and test subsets. Author action: Added Table 3: Baseline Characteristics. Reviewer#1, Concern # 12: Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Author response: We appreciate this comment. Tables 6 and 7 were updated/clarified with consistent captions and explanations of dataset comparisons. We expanded the Discussion to address: Lack of external validation and need for future hospital-based datasets. Potential imputation bias and future use of sensitivity analyses. Importance of ARL interpretability and plans to evaluate feature contributions. Author action: Tables 6–7 revised with rationale for comparing UCI vs Kaggle against different baselines. Expanded Section 6 Limitations and Future Directions. Reviewer#1, Concern # 13: Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Author response: We agree with this comment and have expanded the Discussion to address these limitations. We now discuss the absence of external validation, the potential bias introduced by imputation, and the interpretability of ARL. We also comment on future work to explore feature importance and whether a smaller subset of features could achieve comparable accuracy. Author action: Discussion section expanded with subsections on limitations, imputation bias, and ARL interpretability. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Author response: We thank the reviewer for this valuable suggestion. We have revised the introductory paragraph to better reflect current clinical practices, replacing outdated references (stress test, Holter monitoring) with contemporary modalities such as cardiac CT, RbPET, and coronary angiography. Author action: The Introduction now begins with a discussion of ischemic heart disease pathophysiology and updated diagnostic modalities. Reviewer#1, Concern # 2: Related works This section would benefit greatly from a more condensed presentation of the literature. Author response: We appreciate this suggestion. We revised the Related Works section to streamline the narrative, grouping studies under thematic categories (traditional ML, deep learning, hybrid models, ESN-based, and attention-based methods). Author action: Section 2 was restructured for conciseness while retaining comprehensiveness. Reviewer#1, Concern # 3: Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Author response: We acknowledge this concern. We now clearly define the target variables in both datasets: UCI: angiography-based “num” variable, binarized (0 = absence, 1–4 = presence of disease). Kaggle: “cardio” variable defined by combined clinical assessments (blood pressure, cholesterol, ECG). Author action: Added Section 3.1.4 Definition of Heart Disease in the Datasets. Reviewer#1, Concern # 4: Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? Author response: We appreciate this important comment. We have clarified in Section 3.1.4 how IHD was defined in each dataset (Kaggle: cardio; UCI: num attribute binarized). Missing data handling using the IHD Multiple Imputation Technique is now described. We also added clarification on how Echo State Networks were applied to structured tabular data (not time-series). Figures 1 and 2 were redesigned to show dataset composition, preprocessing, and architecture in greater detail. Author action: Section 3.1.4 updated with disease definition, missingness handling, and ESN applicability. Redesigned Figure 1 (workflow with dataset size, missing values, preprocessing, labels, metrics). Redesigned Figure 2 (detailed HRAESN architecture with ESN + ARL modules). Reviewer#1, Concern # 5: It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Author response: We agree this required clarification. While raw ECG series were not used, we adapted ESNs by treating patient feature vectors as structured sequences, mapping them into reservoir states to capture nonlinear feature dependencies. Author action: Added explanation in Section 3.5 Methodology – Application of ESNs to Tabular Data. Reviewer#1, Concern # 6: Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? Author response: We standardized terminology throughout: Class 0 = no IHD, Class 1 = IHD present. Author action: Updated class definitions consistently across Materials & Methods, Results, and figures. Reviewer#1, Concern # 7: New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Author response: We thank the reviewer. These metrics are now introduced in Evaluation Metrics subsection of Materials and Methods. Author action: Section 3.5 includes definitions of Kappa coefficient and Jaccard index. Reviewer#1, Concern # 8: Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? Author response: We have clarified the figure captions to indicate that Figure 4 reports performance metrics separately for both UCI and Kaggle datasets. Author action: Updated Figure 4 caption as suggested Reviewer#1, Concern # 9: For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Author response: We have now reported 95% confidence intervals using bootstrap resampling (1000 iterations) for all major performance metrics. Author action: Confidence intervals are included in tables as suggested. Reviewer#1, Concern # 10: Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Author response: We agree this was ambiguous. Figure 6 is intended to illustrate HRAESN error rates across datasets, while comparative results with baselines are in Tables 8–9. Author action: Figure 6 caption updated to clarify scope Reviewer#1, Concern # 11: Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Author response: We now provide a table of baseline characteristics (age, sex, cholesterol, blood pressure) for training and test subsets. Author action: Added Table 3: Baseline Characteristics. Reviewer#1, Concern # 12: Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Author response: We appreciate this comment. Tables 6 and 7 were updated/clarified with consistent captions and explanations of dataset comparisons. We expanded the Discussion to address: Lack of external validation and need for future hospital-based datasets. Potential imputation bias and future use of sensitivity analyses. Importance of ARL interpretability and plans to evaluate feature contributions. Author action: Tables 6–7 revised with rationale for comparing UCI vs Kaggle against different baselines. Expanded Section 6 Limitations and Future Directions. Reviewer#1, Concern # 13: Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Author response: We agree with this comment and have expanded the Discussion to address these limitations. We now discuss the absence of external validation, the potential bias introduced by imputation, and the interpretability of ARL. We also comment on future work to explore feature importance and whether a smaller subset of features could achieve comparable accuracy. Author action: Discussion section expanded with subsections on limitations, imputation bias, and ARL interpretability. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN , Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India 16 Sep 2025 Author Response Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is ... Continue reading Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Author response: We thank the reviewer for this valuable suggestion. We have revised the introductory paragraph to better reflect current clinical practices, replacing outdated references (stress test, Holter monitoring) with contemporary modalities such as cardiac CT, RbPET, and coronary angiography. Author action: The Introduction now begins with a discussion of ischemic heart disease pathophysiology and updated diagnostic modalities. Reviewer#1, Concern # 2: Related works This section would benefit greatly from a more condensed presentation of the literature. Author response: We appreciate this suggestion. We revised the Related Works section to streamline the narrative, grouping studies under thematic categories (traditional ML, deep learning, hybrid models, ESN-based, and attention-based methods). Author action: Section 2 was restructured for conciseness while retaining comprehensiveness. Reviewer#1, Concern # 3: Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Author response: We acknowledge this concern. We now clearly define the target variables in both datasets: UCI: angiography-based “num” variable, binarized (0 = absence, 1–4 = presence of disease). Kaggle: “cardio” variable defined by combined clinical assessments (blood pressure, cholesterol, ECG). Author action: Added Section 3.1.4 Definition of Heart Disease in the Datasets. Reviewer#1, Concern # 4: Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? Author response: We appreciate this important comment. We have clarified in Section 3.1.4 how IHD was defined in each dataset (Kaggle: cardio; UCI: num attribute binarized). Missing data handling using the IHD Multiple Imputation Technique is now described. We also added clarification on how Echo State Networks were applied to structured tabular data (not time-series). Figures 1 and 2 were redesigned to show dataset composition, preprocessing, and architecture in greater detail. Author action: Section 3.1.4 updated with disease definition, missingness handling, and ESN applicability. Redesigned Figure 1 (workflow with dataset size, missing values, preprocessing, labels, metrics). Redesigned Figure 2 (detailed HRAESN architecture with ESN + ARL modules). Reviewer#1, Concern # 5: It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Author response: We agree this required clarification. While raw ECG series were not used, we adapted ESNs by treating patient feature vectors as structured sequences, mapping them into reservoir states to capture nonlinear feature dependencies. Author action: Added explanation in Section 3.5 Methodology – Application of ESNs to Tabular Data. Reviewer#1, Concern # 6: Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? Author response: We standardized terminology throughout: Class 0 = no IHD, Class 1 = IHD present. Author action: Updated class definitions consistently across Materials & Methods, Results, and figures. Reviewer#1, Concern # 7: New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Author response: We thank the reviewer. These metrics are now introduced in Evaluation Metrics subsection of Materials and Methods. Author action: Section 3.5 includes definitions of Kappa coefficient and Jaccard index. Reviewer#1, Concern # 8: Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? Author response: We have clarified the figure captions to indicate that Figure 4 reports performance metrics separately for both UCI and Kaggle datasets. Author action: Updated Figure 4 caption as suggested Reviewer#1, Concern # 9: For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Author response: We have now reported 95% confidence intervals using bootstrap resampling (1000 iterations) for all major performance metrics. Author action: Confidence intervals are included in tables as suggested. Reviewer#1, Concern # 10: Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Author response: We agree this was ambiguous. Figure 6 is intended to illustrate HRAESN error rates across datasets, while comparative results with baselines are in Tables 8–9. Author action: Figure 6 caption updated to clarify scope Reviewer#1, Concern # 11: Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Author response: We now provide a table of baseline characteristics (age, sex, cholesterol, blood pressure) for training and test subsets. Author action: Added Table 3: Baseline Characteristics. Reviewer#1, Concern # 12: Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Author response: We appreciate this comment. Tables 6 and 7 were updated/clarified with consistent captions and explanations of dataset comparisons. We expanded the Discussion to address: Lack of external validation and need for future hospital-based datasets. Potential imputation bias and future use of sensitivity analyses. Importance of ARL interpretability and plans to evaluate feature contributions. Author action: Tables 6–7 revised with rationale for comparing UCI vs Kaggle against different baselines. Expanded Section 6 Limitations and Future Directions. Reviewer#1, Concern # 13: Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Author response: We agree with this comment and have expanded the Discussion to address these limitations. We now discuss the absence of external validation, the potential bias introduced by imputation, and the interpretability of ARL. We also comment on future work to explore feature importance and whether a smaller subset of features could achieve comparable accuracy. Author action: Discussion section expanded with subsections on limitations, imputation bias, and ARL interpretability. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Author response: We thank the reviewer for this valuable suggestion. We have revised the introductory paragraph to better reflect current clinical practices, replacing outdated references (stress test, Holter monitoring) with contemporary modalities such as cardiac CT, RbPET, and coronary angiography. Author action: The Introduction now begins with a discussion of ischemic heart disease pathophysiology and updated diagnostic modalities. Reviewer#1, Concern # 2: Related works This section would benefit greatly from a more condensed presentation of the literature. Author response: We appreciate this suggestion. We revised the Related Works section to streamline the narrative, grouping studies under thematic categories (traditional ML, deep learning, hybrid models, ESN-based, and attention-based methods). Author action: Section 2 was restructured for conciseness while retaining comprehensiveness. Reviewer#1, Concern # 3: Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Author response: We acknowledge this concern. We now clearly define the target variables in both datasets: UCI: angiography-based “num” variable, binarized (0 = absence, 1–4 = presence of disease). Kaggle: “cardio” variable defined by combined clinical assessments (blood pressure, cholesterol, ECG). Author action: Added Section 3.1.4 Definition of Heart Disease in the Datasets. Reviewer#1, Concern # 4: Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? Author response: We appreciate this important comment. We have clarified in Section 3.1.4 how IHD was defined in each dataset (Kaggle: cardio; UCI: num attribute binarized). Missing data handling using the IHD Multiple Imputation Technique is now described. We also added clarification on how Echo State Networks were applied to structured tabular data (not time-series). Figures 1 and 2 were redesigned to show dataset composition, preprocessing, and architecture in greater detail. Author action: Section 3.1.4 updated with disease definition, missingness handling, and ESN applicability. Redesigned Figure 1 (workflow with dataset size, missing values, preprocessing, labels, metrics). Redesigned Figure 2 (detailed HRAESN architecture with ESN + ARL modules). Reviewer#1, Concern # 5: It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Author response: We agree this required clarification. While raw ECG series were not used, we adapted ESNs by treating patient feature vectors as structured sequences, mapping them into reservoir states to capture nonlinear feature dependencies. Author action: Added explanation in Section 3.5 Methodology – Application of ESNs to Tabular Data. Reviewer#1, Concern # 6: Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? Author response: We standardized terminology throughout: Class 0 = no IHD, Class 1 = IHD present. Author action: Updated class definitions consistently across Materials & Methods, Results, and figures. Reviewer#1, Concern # 7: New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Author response: We thank the reviewer. These metrics are now introduced in Evaluation Metrics subsection of Materials and Methods. Author action: Section 3.5 includes definitions of Kappa coefficient and Jaccard index. Reviewer#1, Concern # 8: Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? Author response: We have clarified the figure captions to indicate that Figure 4 reports performance metrics separately for both UCI and Kaggle datasets. Author action: Updated Figure 4 caption as suggested Reviewer#1, Concern # 9: For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Author response: We have now reported 95% confidence intervals using bootstrap resampling (1000 iterations) for all major performance metrics. Author action: Confidence intervals are included in tables as suggested. Reviewer#1, Concern # 10: Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Author response: We agree this was ambiguous. Figure 6 is intended to illustrate HRAESN error rates across datasets, while comparative results with baselines are in Tables 8–9. Author action: Figure 6 caption updated to clarify scope Reviewer#1, Concern # 11: Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Author response: We now provide a table of baseline characteristics (age, sex, cholesterol, blood pressure) for training and test subsets. Author action: Added Table 3: Baseline Characteristics. Reviewer#1, Concern # 12: Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Author response: We appreciate this comment. Tables 6 and 7 were updated/clarified with consistent captions and explanations of dataset comparisons. We expanded the Discussion to address: Lack of external validation and need for future hospital-based datasets. Potential imputation bias and future use of sensitivity analyses. Importance of ARL interpretability and plans to evaluate feature contributions. Author action: Tables 6–7 revised with rationale for comparing UCI vs Kaggle against different baselines. Expanded Section 6 Limitations and Future Directions. Reviewer#1, Concern # 13: Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Author response: We agree with this comment and have expanded the Discussion to address these limitations. We now discuss the absence of external validation, the potential bias introduced by imputation, and the interpretability of ARL. We also comment on future work to explore feature importance and whether a smaller subset of features could achieve comparable accuracy. Author action: Discussion section expanded with subsections on limitations, imputation bias, and ARL interpretability. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. Competing Interests: The author(s) declare that they have no competing interests. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 03 Jul 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 Version 2 (revision) 16 Sep 25 read Version 1 03 Jul 25 read read read Amalie Dahl Haue , University of Copenhagen, Copenhagen, Denmark Dhadkan Shrestha , Texas State University College of Science and Engineering, San Marcos, USA MUHAMMAD HAMMAD MEMON , Southwest University of Science and Technology, Sichuan, China Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Shrestha D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Sep 2025 | for Version 2 Dhadkan Shrestha , Texas State University College of Science and Engineering, San Marcos, Texas, USA 0 Views copyright © 2025 Shrestha D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Everything looks good now. Thank you for revising. Competing Interests No competing interests were disclosed. Reviewer Expertise Machine Learning, Artificial Intelligence, Big Data I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 26 Sep 2025 VIJAYA ARJUNAN RANGANATHAN, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Thank you for your kind feedback and approval. View more View less Competing Interests The authors declare that they have no competing interests reply Respond Report a concern Shrestha D. Peer Review Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.187669.r414780) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-650/v2#referee-response-414780 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 MEMON M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Sep 2025 | for Version 1 MUHAMMAD HAMMAD MEMON , Southwest University of Science and Technology, Sichuan, China 0 Views copyright © 2025 MEMON M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Summary of the Article: The manuscript introduces a Hybrid Residual Attention with Echo State Network (HRAESN) model for ischemic heart disease (IHD) prediction. The model integrates Attention Residual Learning (ARL) to enhance feature extraction and Echo State Networks (ESN) for efficient time-series processing. Two datasets are used: the Kaggle Cardiovascular Disease dataset (70,000 samples) and the UCI Heart Disease dataset (303 samples). The authors report very high performance (up to 98.4% accuracy), claiming that HRAESN outperforms traditional ML/DL baselines. The study is relevant and well-motivated, with clear clinical importance. However, there are major concerns regarding methodological rigor, reproducibility, and statistical robustness . Major Concerns Presentation and Literature Coverage The manuscript is generally clear, but the literature review is overly descriptive and includes some weak references (e.g., tutorial websites). Prior work on combining attention and ESN (e.g., Deep Belief Echo-State Networks, Graph Residual Attention) is not sufficiently discussed. The novelty contribution must be better distinguished . Study Design and Technical Soundness The reported performance (>97% accuracy) is unrealistically high for these datasets and suggests possible overfitting or data leakage . Only a single 80:20 train-test split is reported. This is not sufficient for robust evaluation in medical ML. At minimum, k-fold cross-validation with stratified sampling is required. Methods and Replication Details of the Ischemic Heart Disease Multiple Imputation Technique are insufficient. The method is referenced but not described in reproducible detail. No code, model weights, or supplementary scripts are provided, making replication difficult. Statistical Analysis No statistical significance testing (e.g., McNemar’s test, paired t-test, Wilcoxon signed-rank test) is provided. Reported differences may not be statistically meaningful. Metrics such as ROC curves, AUC, calibration plots, and precision-recall curves should be included for clinical interpretability. Reproducibility and Source Data Although the datasets are public, the exact preprocessing steps and imputation pipeline are not fully transparent , which limits reproducibility. PCA plots and confusion matrices are shown but lack supporting raw numbers or code availability. Support for Conclusions While results are promising, conclusions about clinical utility are overstated . Without independent external validation on real hospital datasets, it is premature to suggest readiness for clinical deployment. Limitations such as dataset imbalance, computational cost, and lack of external validation are only briefly acknowledged and need stronger discussion. Minor Comments Some sections could be streamlined (particularly Related Works). Figures would benefit from statistical annotations (e.g., significance levels). The ethics statement should clarify whether the Kaggle dataset contributor had appropriate institutional approval. Writing is generally clear but could be more concise in parts. Recommendations to Improve the Manuscript Re-run experiments with 10-fold cross-validation and report mean ± standard deviation. Add statistical tests to confirm whether improvements over baselines are significant. Provide algorithmic details of the imputation method and release source code/models . Include AUC/ROC, calibration, and PR curves for stronger evaluation. Strengthen the novelty discussion by differentiating HRAESN from earlier ESN+attention studies. Expand the limitations section , especially regarding generalizability and clinical applicability. Final Recommendation Major Revision The study addresses an important healthcare challenge and proposes an interesting hybrid deep learning approach. However, methodological rigor, reproducibility, and statistical analysis must be improved to make the findings scientifically sound and credible for indexing. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Artificial Intelligence and Machine Learning, Medical Data Mining and Predictive Analytics, Deep Learning for Healthcare Applications, Network Security and Cloud Computing. I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#3, Concern # 1: Summary of the Article: Author response: We thank Reviewer for the detailed assessment. The reviewer highlighted the clinical relevance of our work while raising concerns about methodology, reproducibility, and statistical robustness. We carefully revised the manuscript to address all points raised. Below we provide a structured response. Reviewer#3, Concern # 2: Presentation and Literature Coverage Author response: We acknowledge this important observation. The Related Works section has been streamlined and focused on high-quality peer-reviewed studies. We expanded discussion of prior attention+ESN combinations, including Deep Belief Echo-State Networks (DBEN) and Graph Residual Attention models, to clearly distinguish our contribution. Our novelty lies in extending ESNs beyond time-series into structured clinical tabular data, integrated with ARL and combined with a tailored imputation framework. Author action: Revised Section 2 (Related Works) to be more concise, replaced weak/tutorial references with peer-reviewed sources, and explicitly clarified novelty. Reviewer#3, Concern # 3: Study Design and Technical Soundness Author response: We appreciate this concern. To strengthen robustness, we re-ran experiments with 5-fold and 10-fold stratified cross-validation in addition to the 80:20 split. Results are now reported as mean ± standard deviation. Performance remained consistently high, though slightly lower than single-split values, confirming stability without evidence of leakage. Author action: Added cross-validation experiments and updated Tables 7–10 with mean ± SD. Reproducibility pipeline clarified in Section 3.5 Methodology. Reviewer#3, Concern # 4: Methods and Replication Author response: We agree. The Ischemic Heart Disease Multiple Imputation Technique (IHD-MIT) is now described in step-by-step detail (predictor selection, iterative regression, variance preservation). For transparency, we have expanded the methodological description of the IHD-MIT imputation pipeline and model implementation in detail. Author action: Expanded Section 3.5.1 (IHD-MIT) with algorithmic details. Reviewer#3, Concern # 5: Statistical Analysis Author response: We fully agree. We added statistical significance testing (McNemar’s test for paired predictions, Wilcoxon signed-rank across folds) to confirm differences. Additionally, we now report ROC curves, AUC values, and calibration plots for clinical interpretability. Results demonstrate that HRAESN improvements are statistically significant (p < 0.05). Author action: Added Figure 7 for ROC/AUC; included calibration analysis. Expanded Results Section 4.2–4.3 to include statistical testing. Reviewer#3, Concern # 6: Reproducibility and Source Data Author response: We clarified all preprocessing steps, including normalization, imputation, train-test stratification, and cross-validation. Author action: Updated Figures 3–6 captions with supporting details. Reviewer#3, Concern # 7: Support for Conclusions Author response: We agree and have moderated claims. We now clearly state that this work is a proof-of-concept and not clinically deployable yet. We expanded Limitations to address external validation needs, potential bias from imputation, dataset imbalance, computational cost, and the need for interpretability studies. Author action: Expanded Section 6 Limitations and Future Directions, emphasizing generalizability and next steps toward real-world validation. Reviewer#3, Concern # 8: Minor Comments Author response: We thank the reviewer. Related Works was condensed (as above). Figures now include statistical annotations (significance levels). We clarified that Kaggle data are anonymized and released under open license, with ethical approvals obtained by original curators. The manuscript was carefully edited for conciseness. Author action: Revised Section 2, updated figure annotations, clarified Ethics Statement, and streamlined prose throughout. Reviewer#3, Concern # 1: Reviewer Recommendations Implemented Author response: Re-ran experiments with 10-fold cross-validation. Reported mean ± SD for all metrics. Added statistical tests (McNemar, Wilcoxon). Included ROC curves. Provided algorithmic details of IHD-MIT. Strengthened novelty discussion and limitations. · We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. View more View less Competing Interests The author(s) declare that they have no competing interests. reply Respond Report a concern MEMON MH. Peer Review Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r403945) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-403945 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Shrestha D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 04 Sep 2025 | for Version 1 Dhadkan Shrestha , Texas State University College of Science and Engineering, San Marcos, Texas, USA 0 Views copyright © 2025 Shrestha D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Summary of the Article The manuscript presents a Hybrid Residual Attention with Echo State Network (HRAESN) model for predicting ischemic heart disease (IHD). The approach integrates Attention Residual Learning (ARL) for feature extraction with Echo State Networks (ESNs) for efficient time-series learning. The study evaluates performance on two publicly available datasets: the Kaggle Cardiovascular Disease dataset (70,000 records) and the UCI Heart Disease dataset (303 records) . Missing values were handled using a tailored Multiple Imputation Technique . The proposed model achieved high classification performance, with accuracies of 98.4% (Kaggle) and 97.7% (UCI) , surpassing traditional ML and DL baselines. The authors conclude that the model offers strong potential as a clinical decision-support tool for early IHD detection. 2. Evaluation of Key Criteria (a) Clarity, Accuracy, and Literature Coverage Assessment : Yes (with minor improvements suggested) The manuscript is clearly written, structured logically, and cites a broad range of recent literature. The background is thorough and informative. A few parts (e.g., the objectives and problem statement) overlap slightly and could be streamlined for conciseness. Constructive suggestions : Condense repetitive sections to make the narrative flow smoother. More explicitly highlight how this approach differs from other recent hybrid deep learning works to strengthen the novelty claim. (b) Study Design and Technical Soundness Assessment : Yes The study design is technically sound, and the proposed model is innovative. The integration of ARL and ESN is well motivated. The results are very strong, though the extremely high accuracy on the small UCI dataset raises the possibility of overfitting. Still, the use of dropout and a robust hyperparameter setup is a positive point. Suggestions : For added robustness, apply k-fold cross-validation (especially for UCI dataset). Briefly discuss class balance and whether any balancing strategy (e.g., weighting) was needed. (c) Methods and Replicability Assessment : Partly The mathematical formulation is clear, and hyperparameters are well documented. This is very helpful. However, replication would be easier if code or pseudo-code for preprocessing and training were made available. Suggestions : Consider providing code, pseudocode, or a detailed pipeline in supplementary materials. Clarify how hyperparameters were tuned (manual search, grid search, etc.). (d) Statistical Analysis and Interpretation Assessment : Yes The authors present a comprehensive set of performance metrics (accuracy, sensitivity, specificity, F1, Kappa, FAR/FRR), which is commendable. Interpretation is generally appropriate. One minor limitation is the absence of variance/confidence intervals across multiple runs. Suggestions : Indicate whether results are from a single run or averaged across runs. If possible, include confidence intervals or standard deviations. (e) Availability of Source Data Assessment : Yes The datasets (UCI and Kaggle) are publicly available and properly cited. Ethical considerations are addressed. This ensures reproducibility of the raw data. Suggestions : It would be helpful to share the preprocessed datasets or preprocessing scripts used before training. (f) Conclusions and Support from Results Assessment : Yes The conclusions are well supported by the reported results. The performance improvement over baselines is clear. That said, claims about clinical applicability should be framed as potential future applications rather than immediate readiness. Suggestions : Add a brief “Limitations” section noting that real-world hospital validation is pending. Slightly temper statements on clinical deployment to emphasize this is a proof-of-concept 3. Key Points to Address To make the manuscript even stronger, the authors should consider: Adding cross-validation results (especially for the UCI dataset). Reporting variance or confidence intervals for performance metrics. Providing code/pseudocode or preprocessing details for easier replication. Including a short limitations section (dataset size, clinical validation, computational cost). Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Machine Learning, Artificial Intelligence, Big Data I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#2, Concern # 1: Summary of the Article Author response: We thank the reviewer for the accurate and concise summary of our work. We appreciate the recognition of our proposed Hybrid Residual Attention with Echo State Network (HRAESN) model, our methodological contributions (Attention Residual Learning combined with Echo State Networks), and our evaluation using the Kaggle and UCI datasets. We also thank the reviewer for noting our strategy for handling missing values and the strong performance achieved by the model. Reviewer#2, Concern # 2: Clarity, Accuracy, and Literature Coverage Author response: We agree with this suggestion. We revised the Introduction to remove overlap between the problem statement and objectives, improving narrative flow. Additionally, we added a new paragraph at the end of the Introduction to explicitly highlight novelty: (i) integration of ARL with ESNs, (ii) extending ESNs to structured/tabular clinical data, and (iii) introducing an IHD-specific multiple imputation method. Author action: Revised Introduction: merged problem statement + objectives into a concise paragraph. Added final paragraph in Introduction to emphasize novelty. Reviewer#2, Concern # 3: Study Design and Technical Soundness Author response: We thank the reviewer for this important point. To address robustness, we added text in Methods clarifying that k-fold cross-validation (k=5) was performed on the UCI dataset, confirming stable results across folds. We also report class balance: UCI dataset (~54% IHD, ~46% healthy) and Kaggle dataset (~50% each), showing no major imbalance. No resampling or weighting was needed. We further acknowledge the potential risk of overfitting in the Discussion as a limitation. Author action: Added in Section 3.5: description of k-fold cross-validation on UCI dataset. Added in Section 3.1.4: class balance description. Expanded Discussion: limitation noting overfitting risk in small datasets. Reviewer#2, Concern # 4: Methods and Replicability Author response: We appreciate this suggestion. To enhance replicability, we included a pseudo-code style Algorithm (Algorithm 1) in the Methods section, summarizing the preprocessing, model training, and evaluation pipeline. We also clarified that hyperparameters were tuned via grid search, selecting the configuration with the highest validation F1-score. Author action: Added Algorithm 1 (pipeline) in Section 3.5. Clarified hyperparameter tuning strategy (grid search). Reviewer#2, Concern # 5: Statistical Analysis and Interpretation Author response: We agree. Results now explicitly state they are averaged across multiple runs. We also computed 95% confidence intervals for all primary metrics using bootstrap resampling (1000 iterations). Author action: Updated Results to note averaged results across runs. Reviewer#2, Concern # 6: Availability of Source Data Author response: We thank the reviewer for this comment. While raw datasets are already public, we recognize that preprocessing adds value for replication. We now provide a detailed preprocessing description in Methods (Section 3.1.4) and make scripts available upon request. Author action: Expanded Section 3.1.4 with detailed preprocessing description. Reviewer#2, Concern # 7: Conclusions and Support from Results Author response: We agree with this suggestion. The Discussion has been expanded with a new Limitations subsection addressing dataset size, lack of external clinical validation, potential imputation bias, and computational cost. Statements on clinical application have been revised to emphasize that this is a proof-of-concept with potential future clinical use. Author action: Expanded Discussion with Limitations subsection. Rephrased Conclusion to emphasize proof-of-concept, not immediate deployment. Reviewer#2, Concern # 8: Key Points to Address Author response: All these points have been addressed in the revision: Cross-validation results for UCI dataset included. 95% confidence intervals. Algorithm 1 (pseudo-code pipeline) added in Methods. Expanded Discussion with a Limitations section. Author action: Revisions made in Sections 3.1.4, 3.5, Results, and Discussion. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. View more View less Competing Interests The author(s) declare that they have no competing interests. reply Respond Report a concern Shrestha D. Peer Review Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r406539) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-406539 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Haue A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 25 Aug 2025 | for Version 1 Amalie Dahl Haue , University of Copenhagen, Copenhagen, Denmark 0 Views copyright © 2025 Haue A. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The research article by Ranganathan et al. presents a deep learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction derived from analysis of the Kaggle Cardiovascular Disease dataset and the UCI Heart Disease dataset. Their model (HRAEN) demonstrates superior perfomance with accuracy rating between 97.7% and 98.4%. Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Related works This section would benefit greatly from a more condensed presentation of the literature. Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? I cannot comment. A qualified statistician is required. Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Cardiology resident I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 16 Sep 2025 VIJAYA ARJUNAN RANGANATHAN, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction Responses to peer review reports Reviewer#1, Concern # 1: Introduction The very first paragraph is could benefit from being rewritten to ensure a better flow and updated to align with current practise. For example, neither stress test, nor Holter monitoring are used routinely to detect ischemic heart disease (IHD). Rather cardiac CT, RbPET and invasive examinations such as coronary arteriography are being used to assess degree of IHD Author response: We thank the reviewer for this valuable suggestion. We have revised the introductory paragraph to better reflect current clinical practices, replacing outdated references (stress test, Holter monitoring) with contemporary modalities such as cardiac CT, RbPET, and coronary angiography. Author action: The Introduction now begins with a discussion of ischemic heart disease pathophysiology and updated diagnostic modalities. Reviewer#1, Concern # 2: Related works This section would benefit greatly from a more condensed presentation of the literature. Author response: We appreciate this suggestion. We revised the Related Works section to streamline the narrative, grouping studies under thematic categories (traditional ML, deep learning, hybrid models, ESN-based, and attention-based methods). Author action: Section 2 was restructured for conciseness while retaining comprehensiveness. Reviewer#1, Concern # 3: Materials and methods It is not clear how heart disease (presence or absence) was defined in the two cohorts, i.e. which diagnostic tests were used. Author response: We acknowledge this concern. We now clearly define the target variables in both datasets: UCI: angiography-based “num” variable, binarized (0 = absence, 1–4 = presence of disease). Kaggle: “cardio” variable defined by combined clinical assessments (blood pressure, cholesterol, ECG). Author action: Added Section 3.1.4 Definition of Heart Disease in the Datasets. Reviewer#1, Concern # 4: Figure 1 and 2 are not detailed enough, i.e. were all entries (observations) in the two datasets included in the study, what was the degree of missingness, and (again) how was IHD assessed? Author response: We appreciate this important comment. We have clarified in Section 3.1.4 how IHD was defined in each dataset (Kaggle: cardio; UCI: num attribute binarized). Missing data handling using the IHD Multiple Imputation Technique is now described. We also added clarification on how Echo State Networks were applied to structured tabular data (not time-series). Figures 1 and 2 were redesigned to show dataset composition, preprocessing, and architecture in greater detail. Author action: Section 3.1.4 updated with disease definition, missingness handling, and ESN applicability. Redesigned Figure 1 (workflow with dataset size, missing values, preprocessing, labels, metrics). Redesigned Figure 2 (detailed HRAESN architecture with ESN + ARL modules). Reviewer#1, Concern # 5: It is not clear how the Echo State Networkds (ESNs) were applied to the data at hand since not time-series data is introduced. Author response: We agree this required clarification. While raw ECG series were not used, we adapted ESNs by treating patient feature vectors as structured sequences, mapping them into reservoir states to capture nonlinear feature dependencies. Author action: Added explanation in Section 3.5 Methodology – Application of ESNs to Tabular Data. Reviewer#1, Concern # 6: Results and analysis The different classes are not annotated consistently. That is, is "Class 1" "heart disease" (as listed in Materials and methods) or "ischemic heart disease" (as listed in Results and analysis)? Author response: We standardized terminology throughout: Class 0 = no IHD, Class 1 = IHD present. Author action: Updated class definitions consistently across Materials & Methods, Results, and figures. Reviewer#1, Concern # 7: New metrics, such as Kappa score/coefficient and Jaccard coefficient are introduced in this section. They ought to be introduced in Materials and methods. Author response: We thank the reviewer. These metrics are now introduced in Evaluation Metrics subsection of Materials and Methods. Author action: Section 3.5 includes definitions of Kappa coefficient and Jaccard index. Reviewer#1, Concern # 8: Figure 4: Does the figure display the performance of the models on a particular dataset or a combined version? Author response: We have clarified the figure captions to indicate that Figure 4 reports performance metrics separately for both UCI and Kaggle datasets. Author action: Updated Figure 4 caption as suggested Reviewer#1, Concern # 9: For the performance metrics, it would be beneficial to include confidence intervals for assessment of statistical significance. Author response: We have now reported 95% confidence intervals using bootstrap resampling (1000 iterations) for all major performance metrics. Author action: Confidence intervals are included in tables as suggested. Reviewer#1, Concern # 10: Figure 6: The authors state that it converts that the proposed HRAESN model outperforms traditional classifiers in multiple performance aspects. However, only the HRAESN evaluated on the UCI and Kaggle dataset are reported in this figure. Author response: We agree this was ambiguous. Figure 6 is intended to illustrate HRAESN error rates across datasets, while comparative results with baselines are in Tables 8–9. Author action: Figure 6 caption updated to clarify scope Reviewer#1, Concern # 11: Were the test and training sets similar? It would be nice with a table that provides an overview of the baseline characteristics in the different populations. Author response: We now provide a table of baseline characteristics (age, sex, cholesterol, blood pressure) for training and test subsets. Author action: Added Table 3: Baseline Characteristics. Reviewer#1, Concern # 12: Table 6 and 7: The authors ought to argue that the HRAESN is comparable to the existing methods. For example, it is not clear why HRAESN on the UCI Heart Disease Dataset and the Kaggle Cardiovascular Disease dataset are being compared to different existing methods. Further, were the existing methods trained to perform a similar classification task as HRAESN. Again the definition of heart disease/IHD as how it was diagnosed is crucial here, but unfortunately lacking from this version of the manuscript. Author response: We appreciate this comment. Tables 6 and 7 were updated/clarified with consistent captions and explanations of dataset comparisons. We expanded the Discussion to address: Lack of external validation and need for future hospital-based datasets. Potential imputation bias and future use of sensitivity analyses. Importance of ARL interpretability and plans to evaluate feature contributions. Author action: Tables 6–7 revised with rationale for comparing UCI vs Kaggle against different baselines. Expanded Section 6 Limitations and Future Directions. Reviewer#1, Concern # 13: Discussion This section appears to be incomplete. For example, the lack of external validation is not addressed. Further, the use of imputation and the potential bias of the results is not discussed. Finally, there is not evaluation of the impact of the Attention Residual Learning (ARL), i.e., which features were most important in the classification when ARL was performed? And, could this strategy be used to identify a limited set of features that could obtain similar performance metrics? Author response: We agree with this comment and have expanded the Discussion to address these limitations. We now discuss the absence of external validation, the potential bias introduced by imputation, and the interpretability of ARL. We also comment on future work to explore feature importance and whether a smaller subset of features could achieve comparable accuracy. Author action: Discussion section expanded with subsections on limitations, imputation bias, and ARL interpretability. We sincerely thank Reviewer for the constructive and rigorous feedback. All major concerns regarding methodology, reproducibility, and statistical robustness have been addressed with substantial new analyses, expanded methodological detail, and clearer discussion of novelty and limitations. We believe these revisions significantly improve the scientific soundness and transparency of the manuscript. View more View less Competing Interests The author(s) declare that they have no competing interests. reply Respond Report a concern Haue AD. Peer Review Report For: Deep Learning based hybrid residual attention and echo state network for high-accuracy heart disease prediction [version 1; peer review: 2 approved with reservations, 1 not approved] . F1000Research 2025, 14 :650 ( https://doi.org/10.5256/f1000research.182263.r401540) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-650/v1#referee-response-401540 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "Deep Learning based hybrid residual attention...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/14-650/v1" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/14-650/v1&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/14-650/v1" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('D C et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/14-650/v1/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/14-650", templates : { twitter : "Deep Learning based hybrid residual attention and echo state.... D C et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/14-650/v1" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/165575/182263") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "182263"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "406534": 0, "406535": 0, "406532": 0, "406533": 0, "415232": 0, "406540": 0, "406541": 0, "406538": 0, "406539": 7, "406536": 0, "406537": 0, "433982": 0, "433983": 0, "414780": 12, "433980": 0, "414781": 0, "433981": 0, "433978": 0, "414779": 0, "433979": 0, "433986": 0, "433987": 0, "433984": 0, "433985": 0, "399238": 0, "401542": 0, "399239": 0, "401543": 0, "399236": 0, "401540": 29, "399237": 0, "401541": 0, "399234": 0, "401538": 0, "399235": 0, "401539": 0, "399232": 0, "401536": 0, "399233": 0, "401537": 0, "399240": 0, "401544": 0, "399241": 0, "401545": 0, "432046": 0, "418734": 0, "432047": 0, "418735": 0, "432044": 0, "418732": 0, "432045": 0, "418733": 0, "432042": 0, "432043": 0, "418731": 0, "432041": 0, "418740": 0, "432050": 0, "418738": 0, "418739": 0, "432048": 0, "418736": 0, "432049": 0, "418737": 0, "448230": 0, "448231": 0, "448228": 0, "448229": 0, "403950": 0, "403951": 0, "448236": 0, "403948": 0, "448237": 0, "403949": 0, "448234": 0, "403946": 0, "448235": 0, "403947": 0, "448232": 0, "403944": 0, "448233": 0, "403945": 16, "410870": 0, "415223": 0, "410871": 0, "410868": 0, "410869": 0, "410866": 0, "410867": 0, "403952": 0, "410865": 0, "403953": 0, "415230": 0, "415231": 0, "415228": 0, "415229": 0, "415226": 0, "410874": 0, "415227": 0, "415224": 0, "410872": 0, "415225": 0, "410873": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "9aee0e61-d9d1-48f5-aa53-4960f0b75951"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });