Early Prediction of Type 2 Debites Using Non-invasive Lifestyle Factors and Machine Learning

Early Prediction of Type 2 Debites Using Non-invasive Lifestyle Factors and Machine Learning | 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 Early Prediction of Type 2 Debites Using Non-invasive Lifestyle Factors and Machine Learning Ameen Shabhashakhan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8546573/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 Type 2 diabetes mellitus (T2DM) remains one of the most pressing global health challenges, primarily influenced by sedentary behavior, poor dietary habits, and other modifiable lifestyle factors. Early detection of individuals at high risk is vital for timely intervention and effective prevention strategies. In this study, a machine learning–based framework is proposed to predict the likelihood of developing T2DM using only non-invasive, lifestyle-related features such as body mass index (BMI), physical activity, diet, stress levels, sleep duration, hydration, and other behavioral indicators. Unlike conventional approaches that depend on invasive biomarkers such as blood glucose or HbA1c, the proposed model leverages easily obtainable data, making it suitable for large-scale, cost-efficient screening. Multiple algorithms were evaluated, including Linear Regression, Decision Tree, Random Forest, and Gradient Boosting. Among these, the Gradient Boosting model demonstrated superior performance, achieving a mean squared error (MSE) of 11.02 and an R² score of 0.94. Feature importance analysis further revealed that BMI, medical adherence, and physical activity were the most significant contributors to diabetes risk prediction. The findings suggest that integrating non-invasive lifestyle data with advanced machine learning models can serve as an effective approach for predicting T2DM risk. This framework shows potential for deployment within digital health platforms to enhance preventive care and promote early intervention. Computational Biology Artificial Intelligence and Machine Learning Type 2 Diabetes Mellitus (T2DM) Diabetes Prediction Machine Learning Gradient Boosting Lifestyle Factors Non-Invasive Detection Early Diagnosis 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-8546573","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":571183021,"identity":"afd132fc-c50f-49bc-8adf-d5f639fa46b0","order_by":0,"name":"Ameen Shabhashakhan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie2PsWrDMBCGJQzOYuh6U/sKZwIKBeE8SBcJg72oHTtlUJd06RzyEoFOmQWGTk20GrQ4S6YM7tIplMrpbDfZCtUHd+KEPn4dIYHAHwax6/KL+06fzPlKGxedos9X6DKuTtOgcrWo9g05kvFktDFRkths9Vz5lBm/61PAFROkc8JuXx5ElIDL1+/SK2/Fve5znGBANeFoFEYJupwZr1Bd9So3rvwE/zGO9uAVsc2Z3Q0r6BQDEhOGtUK6NCZj9S8pqVOPIOcwxvqA5kPngtU+RQzscu3KNbRHnr5alTZSZ1Nmy13Tznj/+h2CwOk0vqT+ubmA6SWPA4FA4H/wDVWSYAcI9h/FAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0009-0523-1258","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ameen","middleName":"","lastName":"Shabhashakhan","suffix":""}],"badges":[],"createdAt":"2026-01-08 03:45:29","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-8546573/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8546573/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100357782,"identity":"ebad1826-f2d4-488c-b602-314db7e12c50","added_by":"auto","created_at":"2026-01-16 07:20:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":266670,"visible":true,"origin":"","legend":"","description":"","filename":"EarlyPredictionOfType2DiabetesUsingNoninvasiveLifestyleFactorsAndMachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8546573/v1_covered_f4c8c216-9fc8-4c48-8a66-dd03284670d6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eEarly Prediction of Type 2 Debites Using Non-invasive Lifestyle Factors and Machine Learning\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":"Type 2 Diabetes Mellitus (T2DM), Diabetes Prediction, Machine Learning, Gradient Boosting, Lifestyle Factors, Non-Invasive Detection, Early Diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8546573/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8546573/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eType 2 diabetes mellitus (T2DM) remains one of the most pressing global health challenges, primarily influenced by sedentary behavior, poor dietary habits, and other modifiable lifestyle factors. 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