Comparison of different machine learning methods for predicting heart disease

Comparison of different machine learning methods for predicting 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 Research Article Comparison of different machine learning methods for predicting heart disease Aiym Temirbayeva This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5699458/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 Purpose : The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting—on a dataset containing clinical features of patients. The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis. Methods : The study used a dataset of 270 patients with 13 clinical features. The data was preprocessed, and target variables were converted into binary values for classification. The dataset was split into training and test sets in a 70-30 ratio. Five machine learning models were trained and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Confusion matrices were analyzed to gain additional insights into model performance. Results : Logistic Regression and Random Forest achieved the best performance among the models, with accuracies of 86.4% and 80.2%, respectively. Logistic Regression demonstrated an ROC-AUC score of 0.844, while Random Forest scored 0.88. The confusion matrices highlighted the predictive strengths and limitations of each model. Conclusion : Logistic Regression and Random Forest were identified as the most reliable models for predicting heart disease in this dataset. Future work will explore hyperparameter tuning and ensemble methods to further enhance model performance, providing valuable insights for early diagnosis and treatment of cardiovascular diseases. Cardiac & Cardiovascular Systems heart disease prediction machine learning methods logistic regression accuracy Full Text Additional Declarations The authors declare no competing interests. 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-5699458","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":393713354,"identity":"2bb7acaa-da5d-447a-a007-31b558a7c710","order_by":0,"name":"Aiym Temirbayeva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYDACZiBOMGCQAbIOAJkSMsRpeWDAwMPDwJYA0sJDlEWMDxhAWngMQBzCWvjbeUw3JBTY8Njzn/n86kaNBQ8D++GjG/BpkTjMY3YjwSCNh0cid5t1zjGgw3jS0m7gtQai5TBQC+824xw2oBYJoAg+HfIQLf95ePjPPDPO+UeEFgOIlgNA7+cwP85tI0KL4WG2MqCWZB6eG2lmzLl9EjxshPwid/7wtps//tjJsfcffvw551udHD/74WP4vc/AYQBjsUmASfzKQYD9AYzF/IGw6lEwCkbBKBiJAAAMUUIWNc+cWAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-6131-2884","institution":"Astana IT University","correspondingAuthor":true,"prefix":"","firstName":"Aiym","middleName":"","lastName":"Temirbayeva","suffix":""}],"badges":[],"createdAt":"2024-12-23 12:05:06","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5699458/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5699458/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72236307,"identity":"1b95bf0d-2015-4a4c-bad9-3f9c7460caa4","added_by":"auto","created_at":"2024-12-24 05:35:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":322182,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleTitle.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5699458/v1_covered_60b49f4d-878e-4ae5-a4a2-b4006a197b92.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eComparison of different machine learning methods for predicting heart disease\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"heart disease, prediction, machine learning methods, logistic regression, accuracy","lastPublishedDoi":"10.21203/rs.3.rs-5699458/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5699458/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: The study aims to accurately predict the presence of heart disease using machine learning models. The research evaluates and compares the performance of five algorithms—Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, and Gradient Boosting—on a dataset containing clinical features of patients. The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: The study used a dataset of 270 patients with 13 clinical features. The data was preprocessed, and target variables were converted into binary values for classification. The dataset was split into training and test sets in a 70-30 ratio. Five machine learning models were trained and evaluated using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Confusion matrices were analyzed to gain additional insights into model performance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Logistic Regression and Random Forest achieved the best performance among the models, with accuracies of 86.4% and 80.2%, respectively. Logistic Regression demonstrated an ROC-AUC score of 0.844, while Random Forest scored 0.88. The confusion matrices highlighted the predictive strengths and limitations of each model.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: Logistic Regression and Random Forest were identified as the most reliable models for predicting heart disease in this dataset. Future work will explore hyperparameter tuning and ensemble methods to further enhance model performance, providing valuable insights for early diagnosis and treatment of cardiovascular diseases.\u003c/p\u003e","manuscriptTitle":"Comparison of different machine learning methods for predicting heart disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 05:27:00","doi":"10.21203/rs.3.rs-5699458/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":"3fb9832e-9cf9-4668-832c-5fd943ef2028","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41964388,"name":"Cardiac \u0026 Cardiovascular Systems"}],"tags":[],"updatedAt":"2024-12-24T05:27:00+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 05:27:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5699458","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5699458","identity":"rs-5699458","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}