CARDIOPREN: An Explainable Autoencoder–RNN Ensemble Framework for Accurate Cardiovascular Disease Prediction

CARDIOPREN: An Explainable Autoencoder–RNN Ensemble Framework for Accurate Cardiovascular Disease Prediction | 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 Research Article CARDIOPREN: An Explainable Autoencoder–RNN Ensemble Framework for Accurate Cardiovascular Disease Prediction Harpreet Kaur, Amlan Kumar Sarkar, Arun Singh, Jalakanuri Gnaneswar Raju, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7553063/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 Cardiovascular diseases are a leading cause of death throughout the world. Thus, there is a pressing requirement for developing a model for early and effective prediction based on symptoms. Here, a novel system for predicting heart attack using deep learning is proposed which blends a combination of temporal and unsupervised learning approaches in order to enhance diagnostic accuracy. To ensure model validity, proposed methodology includes proper data preprocessing which includes handling missing values, cleaning contradictory records and making use of large patient health databases like UCI Cleveland Heart Disease Dataset and Kaggle Heart Failure Clinical Records Dataset. Feedforward Neural Networks, Recurrent Neural Networks, Autoencoders and a hybrid ensemble model referred as CARDIOPREN are some of the deep learning frameworks which are compared. Feature extraction and dimensional reduction are taken up by autoencoders, while patient histories' sequential patterns are identified by RNNs which are augmented with Long Short-Term Memory units. From the results it is observed that 84%, 85.25%, and 84% accuracy were achieved by FNN, RNN, and Autoencoder-classifier combinations respectively. However, proposed CARDIOPREN model is a combination of Autoencoder and RNN modules which is superior over any other model with accuracy 93.4%, F1-score 0.91 and AUC-ROC 0.95. It is revealed in this study how accurately cardiac attack is predicted when it is combined in ensemble strategies which take up temporal modeling along with feature compression. A robust, universally applicable solution for clinical decision support and early cardiovascular disease diagnosis is offered by CARDIOPREN architecture. Cardiovascular Disease Ensemble Learning RNN LSTM Autoencoder Feature Extraction Pattern Analysis Dimensionality Reduction AUC-ROC Score 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-7553063","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531481976,"identity":"f74d5e37-3ccc-4a0e-bbfb-7cff39e9b991","order_by":0,"name":"Harpreet Kaur","email":"","orcid":"","institution":"Lovely Professional University","correspondingAuthor":false,"prefix":"","firstName":"Harpreet","middleName":"","lastName":"Kaur","suffix":""},{"id":531481977,"identity":"00a01358-ccc7-4bba-943a-d40d86bd4ef9","order_by":1,"name":"Amlan Kumar Sarkar","email":"","orcid":"","institution":"GNA 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Prediction","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":"Cardiovascular Disease, Ensemble Learning, RNN, LSTM, Autoencoder, Feature Extraction, Pattern Analysis, Dimensionality Reduction, AUC-ROC Score","lastPublishedDoi":"10.21203/rs.3.rs-7553063/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7553063/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular diseases are a leading cause of death throughout the world. Thus, there is a pressing requirement for developing a model for early and effective prediction based on symptoms. Here, a novel system for predicting heart attack using deep learning is proposed which blends a combination of temporal and unsupervised learning approaches in order to enhance diagnostic accuracy. To ensure model validity, proposed methodology includes proper data preprocessing which includes handling missing values, cleaning contradictory records and making use of large patient health databases like UCI Cleveland Heart Disease Dataset and Kaggle Heart Failure Clinical Records Dataset. Feedforward Neural Networks, Recurrent Neural Networks, Autoencoders and a hybrid ensemble model referred as CARDIOPREN are some of the deep learning frameworks which are compared. Feature extraction and dimensional reduction are taken up by autoencoders, while patient histories' sequential patterns are identified by RNNs which are augmented with Long Short-Term Memory units. From the results it is observed that 84%, 85.25%, and 84% accuracy were achieved by FNN, RNN, and Autoencoder-classifier combinations respectively. However, proposed CARDIOPREN model is a combination of Autoencoder and RNN modules which is superior over any other model with accuracy 93.4%, F1-score 0.91 and AUC-ROC 0.95. It is revealed in this study how accurately cardiac attack is predicted when it is combined in ensemble strategies which take up temporal modeling along with feature compression. A robust, universally applicable solution for clinical decision support and early cardiovascular disease diagnosis is offered by CARDIOPREN architecture.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e","manuscriptTitle":"CARDIOPREN: An Explainable Autoencoder–RNN Ensemble Framework for Accurate Cardiovascular Disease Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-20 15:02:33","doi":"10.21203/rs.3.rs-7553063/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":"66477049-c6db-428a-ac94-f3f04812fd39","owner":[],"postedDate":"October 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-07T09:55:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-20 15:02:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7553063","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7553063","identity":"rs-7553063","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}