Predicting Diabetes Using Supervised Machine Learning Algorithms

Predicting Diabetes Using Supervised Machine Learning Algorithms | 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 Predicting Diabetes Using Supervised Machine Learning Algorithms Sulaiman Afolabi, Nurudeen Ajadi, Afeez Jimoh, Ibrahim Adenekan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4527374/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 This study explores the application of supervised machine learning algorithms in predicting diabetes, a chronic and life-threatening disease with no definitive cure. We employed three supervised machine learning algorithms which include logistic regression, Random Forest, and k-Nearest Neighbors (KNN) to develop predictive models and identify significant features contributing to diabetes. These models were evaluated based on accuracy, minimal test error, specificity, and sensitivity. Our analysis reveals that KNN achieved the highest performance with accuracy of 88.36%, sensitivity of 87.88%, and specificity of 88.83% among the algorithms tested, making it the most reliable for diabetes prediction in this dataset. The goal of this research is to aid in early detection and management of diabetes by leveraging machine learning on electronic health records. Supervised Learning KNN Logistic 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-4527374","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":311103719,"identity":"6d9c1d61-fb84-449d-9e1d-36bd0580d429","order_by":0,"name":"Sulaiman Afolabi","email":"","orcid":"","institution":"University of Louisiana at Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Sulaiman","middleName":"","lastName":"Afolabi","suffix":""},{"id":311103721,"identity":"f316b6c4-0453-4379-a463-d9ad5920a938","order_by":1,"name":"Nurudeen Ajadi","email":"data:image/png;base64,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","orcid":"","institution":"University of Louisiana at Lafayette","correspondingAuthor":true,"prefix":"","firstName":"Nurudeen","middleName":"","lastName":"Ajadi","suffix":""},{"id":311103723,"identity":"64d54b83-48ac-469c-83ce-332c645e9d2a","order_by":2,"name":"Afeez Jimoh","email":"","orcid":"","institution":"University of Louisiana at Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Afeez","middleName":"","lastName":"Jimoh","suffix":""},{"id":311103725,"identity":"167bfb7c-ebab-428f-a99e-745e97c66cf3","order_by":3,"name":"Ibrahim Adenekan","email":"","orcid":"","institution":"University of Louisiana at Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Ibrahim","middleName":"","lastName":"Adenekan","suffix":""}],"badges":[],"createdAt":"2024-06-04 10:41:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4527374/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4527374/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":58637599,"identity":"30d52790-1ac1-4b35-9442-2899bb631d7d","added_by":"auto","created_at":"2024-06-19 07:06:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":399571,"visible":true,"origin":"","legend":"","description":"","filename":"ArticleCompleted.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4527374/v1_covered_63443a1b-ff1f-47b0-ab56-56692bbd91a4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Diabetes Using Supervised Machine Learning Algorithms","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":"Supervised Learning, KNN, Logistic, Random Forest","lastPublishedDoi":"10.21203/rs.3.rs-4527374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4527374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores the application of supervised machine learning algorithms in predicting diabetes, a chronic and life-threatening disease with no definitive cure. We employed three supervised machine learning algorithms which include logistic regression, Random Forest, and k-Nearest Neighbors (KNN) to develop predictive models and identify significant features contributing to diabetes. These models were evaluated based on accuracy, minimal test error, specificity, and sensitivity. Our analysis reveals that KNN achieved the highest performance with accuracy of 88.36%, sensitivity of 87.88%, and specificity of 88.83% among the algorithms tested, making it the most reliable for diabetes prediction in this dataset. The goal of this research is to aid in early detection and management of diabetes by leveraging machine learning on electronic health records.\u003c/p\u003e","manuscriptTitle":"Predicting Diabetes Using Supervised Machine Learning Algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-19 06:49:58","doi":"10.21203/rs.3.rs-4527374/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":"df563001-b594-411f-931b-f3846b534c88","owner":[],"postedDate":"June 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-19T06:50:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-19 06:49:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4527374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4527374","identity":"rs-4527374","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}