Heart Failure Prediction Using Support Vector Machine

Heart Failure Prediction Using Support Vector Machine | 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 Heart Failure Prediction Using Support Vector Machine Fariba Ghasemi, Seyedhasan Sharifi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6118184/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 failure is a significant global health challenge, requiring effective and early diagnostic tools to improve patient outcomes. In this study, we developed a predictive model for heart failure using Support Vector Machines (SVM), leveraging clinical data from 299 patients. The dataset includes key features such as age, ejection fraction, serum sodium levels, and comorbidities like diabetes and high blood pressure. Our SVM model demonstrated exceptional predictive performance, achieving a training accuracy of 99.7% and a testing accuracy of 99.1%. Artificial Intelligence and Machine Learning Diagnosis Heart Failure Machine Learning SVM. Full Text Additional Declarations The authors declare no competing interests. Ethics approval and consent to participate: The original study containing the dataset analyzed in this manuscript was approved by the Institutional Review Board of Government College University (Faisalabad, Pakistan), and states that the principles of Helsinki Declaration were followed (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181001) The original Kaggle page has referred to the paper published by BMC (https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5) 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. 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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-6118184","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427085552,"identity":"a06f1164-bfac-4139-aae5-fd26f6420e46","order_by":0,"name":"Fariba Ghasemi","email":"","orcid":"","institution":"Islamic Azad University","correspondingAuthor":false,"prefix":"","firstName":"Fariba","middleName":"","lastName":"Ghasemi","suffix":""},{"id":427085553,"identity":"ee2d0bf7-9b2f-4f4e-8d7f-aad517d95656","order_by":1,"name":"Seyedhasan Sharifi","email":"data:image/png;base64,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","orcid":"","institution":"Pediatric Cardiology Subspecialist, Day General Hospital, Tehran, Iran","correspondingAuthor":true,"prefix":"","firstName":"Seyedhasan","middleName":"","lastName":"Sharifi","suffix":""}],"badges":[],"createdAt":"2025-02-27 06:52:42","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-6118184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6118184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78909910,"identity":"5bae4fa3-a80b-44e5-8a41-ddb88aaf2fb1","added_by":"auto","created_at":"2025-03-20 16:14:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":425197,"visible":true,"origin":"","legend":"","description":"","filename":"HeartFailurePredictionUsingSupportVectorMachines.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6118184/v1_covered_2c65c9b3-0e23-4518-9c9d-c328883c8aa1.pdf"}],"financialInterests":"\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate: The original study containing the dataset analyzed in this manuscript was approved by the Institutional Review Board of Government College University (Faisalabad, Pakistan), and states that the principles of Helsinki Declaration were followed (https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0181001) The original Kaggle page has referred to the paper published by BMC (https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-020-1023-5)\u003c/p\u003e","formattedTitle":"\u003cp\u003eHeart Failure Prediction Using Support Vector Machine\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Islamic Azad University, Tehran","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":"Diagnosis, Heart Failure, Machine Learning, SVM.","lastPublishedDoi":"10.21203/rs.3.rs-6118184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6118184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHeart failure is a significant global health challenge, requiring effective and early diagnostic tools to improve patient outcomes. 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