Optimizing Cardiovascular Disease Prediction: Harnessing Random Forest Algorithm with Advanced Feature Selection

Optimizing Cardiovascular Disease Prediction: Harnessing Random Forest Algorithm with Advanced Feature Selection | 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 Optimizing Cardiovascular Disease Prediction: Harnessing Random Forest Algorithm with Advanced Feature Selection Kalaivani B, Ranichitra A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3834700/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 (CVD) stand as prevalent and severe health concerns, significantly impacting individuals. The potential for early diagnosis to prevent or relieve CVDs, thereby reducing mortality rates, underscores its critical role. In this effort, adopting machine learning models to identify risk factors emerges as a promising strategy. Additionally, feature selection methods prove invaluable in identifying crucial attributes, contributing to the reduction of diagnostic expenditures. The analysis in this work was consolidated and improved by using a dataset from Cleveland, Long Beach, VA, Switzerland, Hungarian, and Stat log. In our proposed Method, a hybrid Differential Entropy-based information gain and LASSO algorithm are employed for feature selection. The proposed hybrid model, when combined with machine learning techniques like the Random Forest approach, minimizes data dimensions, improve classification performance, and enhances the efficiency of identifying and training feature sets. Finally, the proposed model produces enhanced performance metrics, encompassing accuracy, precision, and recall. Differential Entropy Feature Selection Information gain LASSO Random Forest 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-3834700","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265386182,"identity":"7cac9f07-a402-4821-afde-e8d747fd4a57","order_by":0,"name":"Kalaivani B","email":"data:image/png;base64,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","orcid":"","institution":"Sri S.Ramasamy Naidu Memorial College,Sattur","correspondingAuthor":true,"prefix":"","firstName":"Kalaivani","middleName":"","lastName":"B","suffix":""},{"id":265386183,"identity":"c465f8c0-d62d-4594-ac4f-1b546ce5a77d","order_by":1,"name":"Ranichitra A","email":"","orcid":"","institution":"Sri S.Ramasamy Naidu Memorial College,Sattur","correspondingAuthor":false,"prefix":"","firstName":"Ranichitra","middleName":"","lastName":"A","suffix":""}],"badges":[],"createdAt":"2024-01-04 13:46:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3834700/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3834700/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49565051,"identity":"acd24047-80a0-4eac-bedd-fe304b630118","added_by":"auto","created_at":"2024-01-13 12:07:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":555249,"visible":true,"origin":"","legend":"","description":"","filename":"OptimizingCardiovascularDiseasePredictionadvanced.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3834700/v1_covered_f06703a1-215f-488e-9641-d69184a01a6a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Optimizing Cardiovascular Disease Prediction: Harnessing Random Forest Algorithm with Advanced Feature Selection","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":"Differential Entropy, Feature Selection, Information gain, LASSO, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-3834700/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3834700/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular diseases (CVD) stand as prevalent and severe health concerns, significantly impacting individuals. 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