Machine Learning Models for Predicting Stroke Risk Among Patients with Coronary Heart Disease

Machine Learning Models for Predicting Stroke Risk Among Patients with Coronary Heart Disease | 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 Machine Learning Models for Predicting Stroke Risk Among Patients with Coronary Heart Disease Maurice Wanyonyi, Dominic Kitavi, Faith Mueni, Zakayo Ndiku This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8864270/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Stroke remains a major global health burden and is a frequent and severe complication among patients with coronary heart disease (CHD). Early identification of individuals at high risk is essential for effective prevention; however, conventional clinical risk models often fail to capture complex nonlinear interactions among clinical and behavioral risk factors. This study aims to develop and evaluate an interpretable hybrid machine learning framework for accurate stroke risk prediction among individuals with CHD. A large real-world dataset comprising 253,680 participants was analyzed. Multiple machine learning algorithms were implemented, including logistic regression, tree-based ensembles, support vector machines, neural networks, and a stacked ensemble architecture. Class imbalance was addressed using stratified sampling combined with the Synthetic Minority Over-sampling Technique. Model performance was evaluated using discrimination , calibration, and scalability metrics, while explainable artificial intelligence techniques were applied to enhance clinical interpretability. The stacked ensemble demonstrated superior predictive performance, achieving an area under the receiver operating characteristic curve of 0.96, precision of 0.91, recall of 0.89, and a Matthews correlation coefficient of 0.82. LightGBM and XGBoost also exhibited strong discriminative ability, with AUC values of 0.94 and 0.95, respectively, while maintaining low computational latency. Explainability analyses identified 1 prior heart attack, body mass index, age, and lifestyle-related factors as key contributors to stroke risk. These findings demonstrate that hybrid ensemble learning combined with explainable artificial intelligence can substantially improve stroke risk prediction in CHD populations. The proposed framework provides clinically interpretable, scalable, and deployable decision support tools that can enhance early intervention strategies and support precision cardiovascular medicine. Health sciences/Cardiology Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Stroke risk prediction Coronary heart disease Machine learning Explainable artificial intelligence Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 25 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 14 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Editor invited by journal 17 Feb, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 13 Feb, 2026 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. 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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-8864270","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625647910,"identity":"0323c7ea-9909-4daa-90e0-b49c80a7764e","order_by":0,"name":"Maurice Wanyonyi","email":"data:image/png;base64,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","orcid":"","institution":"University of Embu","correspondingAuthor":true,"prefix":"","firstName":"Maurice","middleName":"","lastName":"Wanyonyi","suffix":""},{"id":625647911,"identity":"975d5482-e972-4b18-8478-9cc9fdc9a70d","order_by":1,"name":"Dominic Kitavi","email":"","orcid":"","institution":"University of Embu","correspondingAuthor":false,"prefix":"","firstName":"Dominic","middleName":"","lastName":"Kitavi","suffix":""},{"id":625647912,"identity":"4d2d4851-620f-46ac-9a8b-96f2f8cbeb11","order_by":2,"name":"Faith Mueni","email":"","orcid":"","institution":"University of Embu","correspondingAuthor":false,"prefix":"","firstName":"Faith","middleName":"","lastName":"Mueni","suffix":""},{"id":625647913,"identity":"cd724ae2-9981-468d-a9bf-16ce0a2364c8","order_by":3,"name":"Zakayo Ndiku","email":"","orcid":"","institution":"University of Embu","correspondingAuthor":false,"prefix":"","firstName":"Zakayo","middleName":"","lastName":"Ndiku","suffix":""}],"badges":[],"createdAt":"2026-02-12 16:39:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8864270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8864270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107704485,"identity":"6ff514e4-8f38-4cda-917d-6bca56fb00d4","added_by":"auto","created_at":"2026-04-24 08:45:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4624321,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8864270/v1_covered_ae0d7548-a94f-4a40-bab8-2469377a46c0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Machine Learning Models for Predicting Stroke Risk Among Patients with Coronary Heart Disease","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Stroke risk prediction, Coronary heart disease, Machine learning, Explainable artificial intelligence","lastPublishedDoi":"10.21203/rs.3.rs-8864270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8864270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Stroke remains a major global health burden and is a frequent and severe complication among patients with coronary heart disease (CHD). Early identification of individuals at high risk is essential for effective prevention; however, conventional clinical risk models often fail to capture complex nonlinear interactions among clinical and behavioral risk factors. This study aims to develop and evaluate an interpretable hybrid machine learning framework for accurate stroke risk prediction among individuals with CHD. A large real-world dataset comprising 253,680 participants was analyzed. Multiple machine learning algorithms were implemented, including logistic regression, tree-based ensembles, support vector machines, neural networks, and a stacked ensemble architecture. Class imbalance was addressed using stratified sampling combined with the Synthetic Minority Over-sampling Technique. Model performance was evaluated using discrimination , calibration, and scalability metrics, while explainable artificial intelligence techniques were applied to enhance clinical interpretability. 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