DiaHealth: Early Prediction of Type-2 Diabetes with Associated Risk Factors Using Machine Learning and Explainable AI

DiaHealth: Early Prediction of Type-2 Diabetes with Associated Risk Factors Using Machine Learning and Explainable AI | 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 DiaHealth: Early Prediction of Type-2 Diabetes with Associated Risk Factors Using Machine Learning and Explainable AI Marzia Zaman, Md. Jobayer Rahman, Tabia Tanzin Prama, Farhana Farhana, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7399285/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 Background: Diabetes mellitus is a rapidly increasing global health concern with severe medical and socio-economic consequences, particularly in developing nations. Although machine learning based solutions offer potential for early diagnosis, existing systems often lack accuracy, validation in external datasets, or relevance to specific local populations. This study aims to address these limitations by developing a highly accurate and explainable diabetes prediction framework and a clinically validated dataset. Methods: We developed a novel framework for Type-2 diabetes prediction by creating and analyzing a new, clinically validated dataset of real-world health records from respondents across Bangladesh. A hybrid feature selection method was implemented that combines correlation feature selection and forest panelized attributes (CFS-FPA) to identify the most predictive risk factors. A fine-tuned Random Forest classifier was trained on this data, and its performance was validated and benchmarked against two public datasets (PIMA and Ranchi). To ensure clinical utility and transparency, we integrated Explainable AI techniques (SHAP and LIME) to provide local and global explanations for the model's predictions. Results: The classification model achieved exceptional predictive performance on our dataset, with an accuracy of 98.26%. The model demonstrated strong robustness and generalizability on external datasets, achieving 85.5% accuracy on the PIMA dataset and 91.7% on the Ranchi dataset. The explainability analysis successfully identified key risk factors and provided transparent and actionable insights into the rationale behind each prediction. Conclusions: This study successfully developed and validated a dataset and a framework for a highly accurate and interpretable machine learning framework for early prediction of diabetes. Using a novel, locally relevant dataset, our approach provides a transparent and reliable tool that can help clinicians make informed decisions, having significant potential to improve health outcomes in resource-limited settings like Bangladesh. Diabetes Mellitus Machine Learning Explainable AI CFS-FPA Feature Selection Disease Classification 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-7399285","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":505107350,"identity":"43eea8a9-ebc5-44f9-b50c-d641baf7807b","order_by":0,"name":"Marzia Zaman","email":"","orcid":"","institution":"Advanced Intelligent Multidisciplinary Systems (AIMS) Lab, Institute of Research Innovation, Incubation and Commercialization (IRIIC), United International University (UIU)","correspondingAuthor":false,"prefix":"","firstName":"Marzia","middleName":"","lastName":"Zaman","suffix":""},{"id":505107351,"identity":"e82fdcdd-a898-4112-9c55-b6f67a6918e8","order_by":1,"name":"Md. 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Although machine learning based solutions offer potential for early diagnosis, existing systems often lack accuracy, validation in external datasets, or relevance to specific local populations. This study aims to address these limitations by developing a highly accurate and explainable diabetes prediction framework and a clinically validated dataset.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We developed a novel framework for Type-2 diabetes prediction by creating and analyzing a new, clinically validated dataset of real-world health records from respondents across Bangladesh. A hybrid feature selection method was implemented that combines correlation feature selection and forest panelized attributes (CFS-FPA) to identify the most predictive risk factors. A fine-tuned Random Forest classifier was trained on this data, and its performance was validated and benchmarked against two public datasets (PIMA and Ranchi). To ensure clinical utility and transparency, we integrated Explainable AI techniques (SHAP and LIME) to provide local and global explanations for the model's predictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe classification model achieved exceptional predictive performance on our dataset, with an accuracy of 98.26%. The model demonstrated strong robustness and generalizability on external datasets, achieving 85.5% accuracy on the PIMA dataset and 91.7% on the Ranchi dataset. The explainability analysis successfully identified key risk factors and provided transparent and actionable insights into the rationale behind each prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThis study successfully developed and validated a dataset and a framework for a highly accurate and interpretable machine learning framework for early prediction of diabetes. Using a novel, locally relevant dataset, our approach provides a transparent and reliable tool that can help clinicians make informed decisions, having significant potential to improve health outcomes in resource-limited settings like Bangladesh.\u003c/p\u003e","manuscriptTitle":"DiaHealth: Early Prediction of Type-2 Diabetes with Associated Risk Factors Using Machine Learning and Explainable AI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 02:15:40","doi":"10.21203/rs.3.rs-7399285/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":"f9debe2f-9743-4f84-babe-476bb2d427ad","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-15T05:24:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 02:15:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7399285","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7399285","identity":"rs-7399285","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}