Machine Learning Insights for Cardiovascular Risk Prediction in Diabetic Patients: Emphasis on Renal and Cardiac Markers Using Random Forests

Machine Learning Insights for Cardiovascular Risk Prediction in Diabetic Patients: Emphasis on Renal and Cardiac Markers Using Random Forests | 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 Method Article Machine Learning Insights for Cardiovascular Risk Prediction in Diabetic Patients: Emphasis on Renal and Cardiac Markers Using Random Forests Julian Borges This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8650621/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 Objectives Cardiovascular disease remains a leading cause of morbidity and mortality among individuals with diabetes. Although machine learning approaches are increasingly proposed for cardiovascular risk prediction, many published studies report optimistic performance due to inadequate validation and limited reproducibility. This study evaluates whether standard, interpretable machine learning models can predict heart failure mortality when assessed using a rigorously validated and fully reproducible analytic pipeline. Methods Two publicly available datasets from the UCI Machine Learning Repository were analyzed: the Early Stage Diabetes Risk Prediction Dataset (n = 520) and the Heart Failure Clinical Records Dataset (n = 299). The heart failure dataset was used exclusively for model development and outcome evaluation. Logistic regression and random forest classifiers were trained and evaluated using stratified five fold cross validation. All reported performance metrics were computed from pooled out of fold predictions. Preprocessing and any class imbalance handling were performed within training folds only to prevent information leakage. Model discrimination was assessed using the area under the receiver operating characteristic curve, with sensitivity and specificity reported to characterize classification tradeoffs. Results Under pooled out of fold evaluation, the random forest model demonstrated higher discriminative performance than logistic regression (AUC 0.91 versus 0.86). Random forest exhibited higher specificity, whereas logistic regression showed higher sensitivity, reflecting distinct error profiles across models. Feature importance analyses and SHAP based explanations consistently identified serum creatinine, ejection fraction, age, and follow up time as dominant predictors of heart failure mortality. Medical Informatics Artificial Intelligence and Machine Learning Bioinformatics Oncology Machine learning cardiovascular disease diabetes heart failure five fold cross validation logistic regression random forest reproducibility Full Text Additional Declarations The authors declare no competing interests. Supplementary Files EarlyStageDiabetesRiskPredictionUCIMachineLearningRepository.pdf HeartFailureClinicalRecordsUCIMachineLearningRepository.pdf README.md 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-8650621","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":577549733,"identity":"f58a724f-0153-4ec1-a65e-a555652e3c6b","order_by":0,"name":"Julian 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Forests\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":"Machine learning, cardiovascular disease, diabetes, heart failure, five fold cross validation, logistic regression, random forest, reproducibility","lastPublishedDoi":"10.21203/rs.3.rs-8650621/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8650621/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eCardiovascular disease remains a leading cause of morbidity and mortality among individuals with diabetes. Although machine learning approaches are increasingly proposed for cardiovascular risk prediction, many published studies report optimistic performance due to inadequate validation and limited reproducibility. This study evaluates whether standard, interpretable machine learning models can predict heart failure mortality when assessed using a rigorously validated and fully reproducible analytic pipeline.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTwo publicly available datasets from the UCI Machine Learning Repository were analyzed: the Early Stage Diabetes Risk Prediction Dataset (n\u0026thinsp;=\u0026thinsp;520) and the Heart Failure Clinical Records Dataset (n\u0026thinsp;=\u0026thinsp;299). The heart failure dataset was used exclusively for model development and outcome evaluation. Logistic regression and random forest classifiers were trained and evaluated using stratified five fold cross validation. All reported performance metrics were computed from pooled out of fold predictions. Preprocessing and any class imbalance handling were performed within training folds only to prevent information leakage. Model discrimination was assessed using the area under the receiver operating characteristic curve, with sensitivity and specificity reported to characterize classification tradeoffs.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUnder pooled out of fold evaluation, the random forest model demonstrated higher discriminative performance than logistic regression (AUC 0.91 versus 0.86). Random forest exhibited higher specificity, whereas logistic regression showed higher sensitivity, reflecting distinct error profiles across models. Feature importance analyses and SHAP based explanations consistently identified serum creatinine, ejection fraction, age, and follow up time as dominant predictors of heart failure mortality.\u003c/p\u003e","manuscriptTitle":"Machine Learning Insights for Cardiovascular Risk Prediction in Diabetic Patients: Emphasis on Renal and Cardiac Markers Using Random Forests","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 18:25:45","doi":"10.21203/rs.3.rs-8650621/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":"a1b2541f-21ce-4dee-8b64-0ef225e0c118","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61446959,"name":"Medical Informatics"},{"id":61446960,"name":"Artificial Intelligence and Machine Learning"},{"id":61446961,"name":"Bioinformatics"},{"id":61446962,"name":"Oncology"}],"tags":[],"updatedAt":"2026-01-21T18:25:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 18:25:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8650621","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8650621","identity":"rs-8650621","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}