ML driven approach for Post-Transplant Surveillance and Risk Modelling in Heart Patients

ML driven approach for Post-Transplant Surveillance and Risk Modelling in Heart Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article ML driven approach for Post-Transplant Surveillance and Risk Modelling in Heart Patients Dr.K.LATHA Dr.K.LATHA,Assistant Professor(Sl.Grade) This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8391856/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Heart transplantation is the most effective treatment for patients with end-stage heart failure, yet several ongoing challenges limit its success. These include improving donor-recipient compatibility, evaluating surgical risk accurately, and forecasting long-term outcomes reliably. Traditional clinical methods often focus on surgical logistics and lack data-driven tools to guide decision-making throughout the transplant journey. To overcome these limitations, this study presents the Hybrid-XR Transplant System—a modular, machine learning–based framework combining XGBoost and Random Survival Forest (RSF) algorithms. The system addresses four key areas: donor-recipient matching, pre-operative risk assessment, post-transplant monitoring, and survival prediction. XGBoost is used for classification tasks such as compatibility and risk level, while RSF is applied to survival analysis, offering improved accuracy over traditional statistical models. Each component was rigorously validated using diverse evaluation metrics. Classification performance was measured using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Matthews Correlation Coefficient (MCC). For survival analysis, the Concordance Index (C-Index) and Log-Rank Test were used. Hybrid-XR consistently outperformed Support Vector Machines, Logistic Regression, Decision Trees, and the Cox model. By integrating XGBoost-based classification and Random Survival Forest survival analysis, it enables robust interpretation of complex clinical data and supports personalized, data-driven decision-making in heart transplantation. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Extreme Gradient Boosting Random Survival Forest (RSF) Post-Transplant Surveillance Machine Learning Hybrid-XR Transplant System Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8391856","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":611440897,"identity":"8141cfe2-40a2-41fa-a8be-a17cffbc5ad8","order_by":0,"name":"Dr.K.LATHA Dr.K.LATHA,Assistant Professor(Sl.Grade)","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3OMYvCMBTA8dclLqWuLRz6FVIEXSr3VV4pdHJwc7QfQOjqfYvCQecnD3QJNxfuhuvi5OZSwcFYnUSibg75k2QI+fECYLO9YyQc0id0AQRge9MuI9EPkCDIzgRfIZI0gSsx5v2ukQ7NX3+wUcP/uoGeV6HDUwMJflJaLXAblmoykvpjg6BC4KWBSNXJyEV2SpoMfU3i4kzcB2R1RP78znctmT9BBLGeEhf+ZQrKRyRQAvkj5WRZbWcSUz/8UnVmJJ4SYb2LeJznSVk3UdT3NgnvTeQ2X28newHYbDab7V4nzt9bcyzX9K8AAAAASUVORK5CYII=","orcid":"","institution":"University College of Engineering,BIT Campus,Anna University, Tiruchirappalli,Tamilnadu,India.","correspondingAuthor":true,"prefix":"Dr.","firstName":"Assistant","middleName":"Professor(Sl.Grade) K.LATHA","lastName":"Dr.K.LATHA","suffix":""}],"badges":[],"createdAt":"2025-12-18 06:38:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8391856/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8391856/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106095113,"identity":"0e4e531b-bb1e-4f4b-8821-fc54f58e9d75","added_by":"auto","created_at":"2026-04-03 11:44:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1165760,"visible":true,"origin":"","legend":"","description":"","filename":"MLPOSTTRANSPLANTINHEARTTRANSPLANT22725.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8391856/v1_covered_16b4ebb7-96ec-4b9c-aa88-88f1162376d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ML driven approach for Post-Transplant Surveillance and Risk Modelling in Heart Patients","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Extreme Gradient Boosting, Random Survival Forest (RSF), Post-Transplant Surveillance, Machine Learning, Hybrid-XR Transplant System","lastPublishedDoi":"10.21203/rs.3.rs-8391856/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8391856/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeart transplantation is the most effective treatment for patients with end-stage heart failure, yet several ongoing challenges limit its success. These include improving donor-recipient compatibility, evaluating surgical risk accurately, and forecasting long-term outcomes reliably. Traditional clinical methods often focus on surgical logistics and lack data-driven tools to guide decision-making throughout the transplant journey. To overcome these limitations, this study presents the Hybrid-XR Transplant System\u0026mdash;a modular, machine learning\u0026ndash;based framework combining XGBoost and Random Survival Forest (RSF) algorithms.\u003c/p\u003e \u003cp\u003eThe system addresses four key areas: donor-recipient matching, pre-operative risk assessment, post-transplant monitoring, and survival prediction. XGBoost is used for classification tasks such as compatibility and risk level, while RSF is applied to survival analysis, offering improved accuracy over traditional statistical models. Each component was rigorously validated using diverse evaluation metrics. Classification performance was measured using Accuracy, Precision, Recall, F1-Score, AUC-ROC, and Matthews Correlation Coefficient (MCC). For survival analysis, the Concordance Index (C-Index) and Log-Rank Test were used.\u003c/p\u003e \u003cp\u003eHybrid-XR consistently outperformed Support Vector Machines, Logistic Regression, Decision Trees, and the Cox model. By integrating XGBoost-based classification and Random Survival Forest survival analysis, it enables robust interpretation of complex clinical data and supports personalized, data-driven decision-making in heart transplantation.\u003c/p\u003e","manuscriptTitle":"ML driven approach for Post-Transplant Surveillance and Risk Modelling in Heart Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 19:27:52","doi":"10.21203/rs.3.rs-8391856/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9b1ff8cd-685c-405d-94a0-e4f01a8ab698","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65058198,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":65058199,"name":"Health sciences/Health care"},{"id":65058200,"name":"Physical sciences/Mathematics and computing"},{"id":65058201,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-04-03T05:10:01+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 19:27:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8391856","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8391856","identity":"rs-8391856","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}