Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort

Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort | 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 Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort Richmond Balinia Adda This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8019155/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: Type 2 diabetes mellitus (T2DM) is an escalating public health concern across Africa, but regionally tailored predictive models are scarce. Advances in machine learning (ML) offer potential for early identification, though previous research has been constrained by methodological issues such as data leakage, class imbalance, and overfitting, limiting clinical deployment, especially in digital health contexts. Methods: This study analysed data from 2,010 participants in the H3Africa AWI-Gen cohort in northern Ghana to develop and evaluate ML-based prediction models tailored to African settings. Rigorous preprocessing steps, including handling class imbalance with SMOTE and excluding diagnostic biomarkers prone to target leakage, were applied. Eight ML classifiers underwent robust Bayesian hyperparameter optimisation. Model performance was assessed via stratified 5-fold cross-validation and confirmed through extensive sensitivity and calibration analyses. Results: The optimised XGBoost model yielded an AUC of 0.845 (95% CI: 0.812–0.878) and a sensitivity of 78.2% on unseen data. Including glucose as a predictor increased performance by 11.5%, underscoring the necessity of its exclusion to avoid biased evaluation. Models using only anthropometric and lifestyle variables (AUC = 0.783) demonstrated robust predictive capacity, with waist circumference, physical activity, and BMI standing out as the most stable predictors across analyses. Conclusion: Our findings demonstrate that ML models constructed from routinely collected clinical and lifestyle data can attain clinically meaningful diabetes prediction suitable for digital health applications in low-resource African contexts. This study addresses prior methodological gaps and offers a data-driven framework that is both robust and clinically plausible for early T2DM detection, with potential implications for public health policy and digital screening programmes in similar populations. Artificial Intelligence and Machine Learning Biostatistics Medical Informatics Machine Learning Type 2 Diabetes Digital Health Predictive Modelling Africa Clinical Validation XGBoost 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-8019155","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539190420,"identity":"14ae48ef-cf93-4a16-bb54-495fa183d110","order_by":0,"name":"Richmond Balinia Adda","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYDACZgY2EMXYwJBgwPAByGJjJ0UL4wyQFmbC9iC0MPNADMEPzNuZnz26UXFHtr89eeNnm1/b5PmYGRg/fMzBrUXmMJu5cc6ZZ8Yzzjwrls7tu23YxszALDlzG24tEsw8bNK5bYcTG27kGEjn9txmBGphY+YlRsv8GznGvy17btsTr2XDjRwzaYYftxOJ0MJmJp1z5rDxxjPPyix7G24ntzEzNuP3C//hZ9I5FYdl5x1P3nzjx5/btvPbmw9++IhHCypgbAOTDcSqB4E/pCgeBaNgFIyCkQIAioVPt8eBAK8AAAAASUVORK5CYII=","orcid":"","institution":"Department of Biometry, School of Mathematical Sciences, C. 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Tedam University of Technology and Applied Sciences, Navrongo, Ghana","correspondingAuthor":true,"prefix":"","firstName":"Richmond","middleName":"Balinia","lastName":"Adda","suffix":""}],"badges":[],"createdAt":"2025-11-03 12:31:17","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-8019155/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8019155/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95223884,"identity":"01102a95-1f09-4582-812c-00c5211c1f5e","added_by":"auto","created_at":"2025-11-05 16:23:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":402255,"visible":true,"origin":"","legend":"","description":"","filename":"MachineLearning.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8019155/v1_covered_f375b664-b711-4b5a-9d64-7b21551721cb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImproving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Navrongo Health Research Centre","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":"Machine Learning, Type 2 Diabetes, Digital Health, Predictive Modelling, Africa, Clinical Validation, XGBoost","lastPublishedDoi":"10.21203/rs.3.rs-8019155/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8019155/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eType 2 diabetes mellitus (T2DM) is an escalating public health concern across Africa, but regionally tailored predictive models are scarce. Advances in machine learning (ML) offer potential for early identification, though previous research has been constrained by methodological issues such as data leakage, class imbalance, and overfitting, limiting clinical deployment, especially in digital health contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis study analysed data from 2,010 participants in the H3Africa AWI-Gen cohort in northern Ghana to develop and evaluate ML-based prediction models tailored to African settings. Rigorous preprocessing steps, including handling class imbalance with SMOTE and excluding diagnostic biomarkers prone to target leakage, were applied. Eight ML classifiers underwent robust Bayesian hyperparameter optimisation. Model performance was assessed via stratified 5-fold cross-validation and confirmed through extensive sensitivity and calibration analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe optimised XGBoost model yielded an AUC of 0.845 (95% CI: 0.812–0.878) and a sensitivity of 78.2% on unseen data. Including glucose as a predictor increased performance by 11.5%, underscoring the necessity of its exclusion to avoid biased evaluation. Models using only anthropometric and lifestyle variables (AUC = 0.783) demonstrated robust predictive capacity, with waist circumference, physical activity, and BMI standing out as the most stable predictors across analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our findings demonstrate that ML models constructed from routinely collected clinical and lifestyle data can attain clinically meaningful diabetes prediction suitable for digital health applications in low-resource African contexts. This study addresses prior methodological gaps and offers a data-driven framework that is both robust and clinically plausible for early T2DM detection, with potential implications for public health policy and digital screening programmes in similar populations.\u003c/p\u003e","manuscriptTitle":"Improving Type 2 Diabetes Prediction: Comparative Evaluation of Machine Learning Classifiers Using Balanced Data from the AWI-Gen Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-04 09:27:10","doi":"10.21203/rs.3.rs-8019155/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":"ba394ebd-b750-419f-aee5-f2fd5bbd0a7d","owner":[],"postedDate":"November 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57342401,"name":"Artificial Intelligence and Machine Learning"},{"id":57342402,"name":"Biostatistics"},{"id":57342403,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2025-11-04T09:27:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-04 09:27:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8019155","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8019155","identity":"rs-8019155","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}