A Multilingual Predictive System for Type 2 Diabetes using the CATBoost Machine Learning Algorithm

CATBoost-Based Multilingual System for Predicting Type 2 Diabetes | 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 CATBoost-Based Multilingual System for Predicting Type 2 Diabetes Julius Olasunmibo Ogunniyi, Olusogo Julius Adetunji, Omolayo Michael Ikumapayi, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6195549/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Predictive Systems have demonstrated potential in predicting Type 2 diabetes (T2D), yet they face various limitations that impact prediction reliability and accessibility. Previous works have not sufficiently addressed incorporating multilingual capacities, such as the Yorùbá language, or utilising loc al datasets in developing these systems. This study is aimed at addressing those named problems by creating a multilingual predictive system for T2D patients, leveraging the CATBoost machine learning algorithm to enhance prediction accuracy and inclusivity. This study employed datasets from several hospitals and a community in Ogbomoso and Akure, totalling 1,197 records, and examined 13 risk factors. Four machine learning algorithms which include Decision Tree, Logistic Regression, Naïve Bayes and CATBoost were employed for non-invasive and invasive methods. The invasive method refers to the development of a model with the inclusion of blood glucose measurement while the non-invasive method develops a model with external factors like age, blood pressure, and lifestyle data. The system was implemented in both English and Yorùbá languages. Evaluation metrics included accuracy, MCC, AUC, recall, Kappa, precision and F1-Score. The two methods were compared using a paired sample t-test and Wilcoxon signed-ranked test. For the non-invasive methods, CATBoost achieved an accuracy of 90.60%, an AUC of 0.9032, a recall of 0.6591, a precision of 0.9073, an F1-Score of 0.7622, a Kappa of 0.7054, and MCC of 0.7203. for the invasive method, CATBoost achieved an accuracy of 97.57%, an AUC of 0.9865, a recall of 0.9789, a precision of 0.9798, an F1-Score of 0.9789, a Kappa of 0.9503, and an MCC of 0.951. This study developed a Predictive System for early prediction of Type 2 diabetes. The system is applicable for diabetes screening in both English and Yorùbá. Computer Architecture and Engineering CATBoost Machine Learning Predictive system Type 2 diabetes Yorùbá language Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-6195549","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427243326,"identity":"9bf066aa-082d-4f7f-ab15-9bd9a7ace994","order_by":0,"name":"Julius Olasunmibo Ogunniyi","email":"","orcid":"","institution":"Elizade University Ilara- Mokin","correspondingAuthor":false,"prefix":"","firstName":"Julius","middleName":"Olasunmibo","lastName":"Ogunniyi","suffix":""},{"id":427243327,"identity":"84d85cb2-a41e-471c-8f3e-4b814d5d7fd5","order_by":1,"name":"Olusogo Julius 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Diabetes\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Elizade University, Ilara- Mokin","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":"CATBoost, Machine Learning, Predictive system, Type 2 diabetes, Yorùbá language","lastPublishedDoi":"10.21203/rs.3.rs-6195549/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6195549/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePredictive Systems have demonstrated potential in predicting Type 2 diabetes (T2D), yet they face various limitations that impact prediction reliability and accessibility. Previous works have not sufficiently addressed incorporating multilingual capacities, such as the Yorùbá language, or utilising loc al datasets in developing these systems. This study is aimed at addressing those named problems by creating a multilingual predictive system for T2D patients, leveraging the CATBoost machine learning algorithm to enhance prediction accuracy and inclusivity. This study employed datasets from several hospitals and a community in Ogbomoso and Akure, totalling 1,197 records, and examined 13 risk factors. Four machine learning algorithms which include Decision Tree, Logistic Regression, Naïve Bayes and CATBoost were employed for non-invasive and invasive methods. The invasive method refers to the development of a model with the inclusion of blood glucose measurement while the non-invasive method develops a model with external factors like age, blood pressure, and lifestyle data. \u0026nbsp;The system was implemented in both English and Yorùbá languages. Evaluation metrics included accuracy, MCC, AUC, recall, Kappa, precision and F1-Score. The two methods were compared using a paired sample t-test and Wilcoxon signed-ranked test. For the non-invasive methods, CATBoost achieved an accuracy of 90.60%, an AUC of 0.9032, a recall of 0.6591, a precision of 0.9073, an F1-Score of 0.7622, a Kappa of 0.7054, and MCC of 0.7203. for the invasive method, CATBoost achieved an accuracy of 97.57%, an AUC of 0.9865, a recall of 0.9789, a precision of 0.9798, an F1-Score of 0.9789, a Kappa of 0.9503, and an MCC of 0.951. This study developed a Predictive System for early prediction of Type 2 diabetes. 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