Diabetes Prediction Through Machine Learning and Ontology

Diabetes Prediction Through Machine Learning and Ontology | 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 Diabetes Prediction Through Machine Learning and Ontology Vishal A. Wankhede¹, Anant R. More, Pankaj S. Desai, Nilesh R. Thakre, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8993860/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 16 You are reading this latest preprint version Abstract Diabetes is a chronic metabolic disorder with a growing global impact on healthcare. Early detection and timely intervention are critical in preventing severe complications and improving patient outcomes. Recently, machine learning techniques and data framework-based approaches have played an important role in medical science by creating automated systems to identify diabetic patients. This paper reviews and compares popular machine learning methods and data framework-based classification techniques. The algorithms studied include Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Artificial Neural Networks (ANN), Naive Bayes, Logistic Regression, and Decision Trees. The performance was measured using metrics like Recall, Accuracy, Precision, and F-Measure from the confusion matrix. This study evaluates six machine learning models on 768 samples from Kaggle's Pima Indian Diabetes Dataset. The results indicate that Ontology-based classification and SVM achieved the highest accuracy, making them highly effective for diabetes prediction. Machine learning Ontology diabetes prediction Full Text Additional Declarations No competing interests reported. Supplementary Files diabetesdataset.xlsx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviews received at journal 05 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviews received at journal 04 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviewers agreed at journal 26 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor invited by journal 13 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 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-8993860","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611201307,"identity":"7d8c037d-3d49-4f54-bc5a-f883f104eecd","order_by":0,"name":"Vishal A. 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