Predictive Modeling for Healthcare: Leveraging Categorical and Binary Data for Enhanced Accuracy

Predictive Modeling for Healthcare: Leveraging Categorical and Binary Data for Enhanced Accuracy | 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 Predictive Modeling for Healthcare: Leveraging Categorical and Binary Data for Enhanced Accuracy Tanuj Saxena, Ujjwal Maurya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5529811/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 Such dramatic growth in healthcare data has increased the necessity of developing predictive models capable of manipulating and processing categorical and binary data, which would constitute significant improvements in the predictability of diseases and patient management strategies. Traditional statistical approaches such as Logistic Regression have been utilized frequently but often fail when managing these complex relationships inherent to large health care datasets. Several studies recently shown that, in comparison with traditional methods, advanced machine learning techniques such as Random Forest, CatBoost, and Gradient Boosting can better capture complex, non-linear patterns and data interactions toward a high level of predictive accuracy. This paper systematically applies and evaluates these new techniques on healthcare datasets while focusing on the optimization of main performance metrics: accuracy, sensitivity, and specificity. Experimental results demonstrate that ensemble models, especially CatBoost, outperform traditional models in terms of prediction accuracy, and thus can be adapted to robust solutions in patient risk classification and early detection of diseases. The findings imply the importance of advanced machine learning methods in supporting the personalization of treatment plans and allocation of resources, leading towards exact, data-driven decision-making in healthcare systems. Machine Learning Healthcare Analytics Disease Prediction Ensemble Learning Classification Algorithms 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. 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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-5529811","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391285247,"identity":"f019074e-e0f0-4d58-9097-e4c113288687","order_by":0,"name":"Tanuj Saxena","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIiWNgGAWjYLACCQQzgYEfTBXgVs2DoUWyAUQZENCCBBIYDA6AaDxa7NnPHpOwYKiTNzjee/AD4540e+PzqxM/PDBgkOcXO4DdFp68NAkJhsOGM3vOJUswPMtJ3Hbj7WYJoMMMZ85OwOGwHDOglgOM/RI5BkC6IsHsxtkNIC0JBrdxaOF/A9JSZ98m/8b4B1CLvfGMs5t/4NUiAbaFObFfgscMaEsO4wb+3m34bbnxxthCwuBw8syeHDOLhANpiTNu8G6zSDCQwOkX9v4cw9sSFXW2G46fMb7x4UCyPX//2c03f1TYyPNLY9cCAswSsFgAq5GAkDiVgwDjBxQu/wG8qkfBKBgFo2DkAQA1d1f7oBIAwQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0002-1579-3261","institution":"Sharda University Greater Noida","correspondingAuthor":true,"prefix":"","firstName":"Tanuj","middleName":"","lastName":"Saxena","suffix":""},{"id":391285248,"identity":"5aa3c3b8-cd71-4a3b-8530-6c3513516eba","order_by":1,"name":"Ujjwal Maurya","email":"","orcid":"https://orcid.org/0009-0005-5629-2449","institution":"Sharda University Greater Noida","correspondingAuthor":false,"prefix":"","firstName":"Ujjwal","middleName":"","lastName":"Maurya","suffix":""}],"badges":[],"createdAt":"2024-11-26 17:23:18","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-5529811/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5529811/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71726848,"identity":"a65a2e7c-b275-41b3-89c7-189ba3e44f7e","added_by":"auto","created_at":"2024-12-18 06:17:24","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":345080,"visible":true,"origin":"","legend":"","description":"","filename":"predictivemodeling.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5529811/v1_covered_db84dc19-c4c9-4ff8-a744-e2bf930efec6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Predictive Modeling for Healthcare: Leveraging Categorical and Binary Data for Enhanced Accuracy","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Sharda University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"Machine Learning, Healthcare Analytics, Disease Prediction, Ensemble Learning, Classification Algorithms","lastPublishedDoi":"10.21203/rs.3.rs-5529811/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5529811/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Such dramatic growth in healthcare data has increased the necessity of developing predictive models capable of manipulating and processing categorical and binary data, which would constitute significant improvements in the predictability of diseases and patient management strategies. 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