{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T16:13:09Z","timestamp":1788279189972,"version":"build-2803163510"},"reference-count":29,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Diabetes mellitus is a chronic metabolic disorder characterized by impaired regulation of blood glucose due to defects in insulin secretion, insulin action, or both. Physiological and lifestyle factors vary among individuals. General medicine is not applicable to all patients. In this scenario, personalized medicine for each individual becomes costly. Effective management of continuous glucose levels with accurate insulin dosage is challenging. To overcome this, a digital twin (DT)-based insulin dosage simulator with an individual\u2019s metabolic system is proposed in this work. Methods: Various machine learning techniques, mathematical models of physiology, and risk assessment using probability are used to predict the dynamics of patient-specific glucose\u2013insulin. Parameters such as carbohydrate intake, sleep patterns, medications, and physical activity were incorporated into this model to capture real-world variations in daily life. For glucose\u2013insulin interactions, the Bergman Minimal Model (BMM) is used; for time-of-day variability, a circadian insulin sensitivity model is used; and for predicting metabolic risks, Bayesian risk estimation (BRE) is used, which includes hyperglycemia risk. To enhance transparency and interpret model predictions, explainable artificial intelligence (XAI) methods are employed. Results: The simulation results showed improved glucose prediction accuracy, enhanced detection of hypoglycemia risk, and optimized insulin dosing strategies compared with traditional approaches. Conclusions: Overall, the proposed digital twin model offers a scalable solution using the latest techniques A \u201cPrescriptive Analytical Framework\u201d is provided using the BMM and BRE for personalized diabetes management and decision support for clinicians.<\/jats:p>","DOI":"10.3390\/diagnostics16172813","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T15:08:43Z","timestamp":1788275323000},"page":"2813","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Hybrid Digital Twin Framework for Personalized Diabetes Management Using Mathematical Modelling and Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"given":"Vathana","family":"Dennish","sequence":"first","affiliation":[{"name":"Department of Computing Technologies, SRM Institute of Science and Technologies, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Babu","family":"Subramani","sequence":"additional","affiliation":[{"name":"Department of Computing Technologies, SRM Institute of Science and Technologies, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3929-8495","authenticated-orcid":false,"given":"Vijayakumar","family":"Ponnusamy","sequence":"additional","affiliation":[{"name":"Department of ECE, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0374-4190","authenticated-orcid":false,"given":"Suganthi","family":"Kuppusamy","sequence":"additional","affiliation":[{"name":"Department of ECE, Faculty of Engineering & Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1355-5833","authenticated-orcid":false,"given":"Janardhanan Subramonia","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of General Medicine, SRM Medical College Hospital and Research Center, Kattankulathur, Chennai 603203, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2631-6308","authenticated-orcid":false,"given":"Nemanja","family":"Zdravkovi\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Belgrade Metropolitan University, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0912-9518","authenticated-orcid":false,"given":"Milo\u0161","family":"Kosti\u0107","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Belgrade Metropolitan University, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"25478","DOI":"10.1038\/s41598-024-76584-7","article-title":"One-year outcomes of a digital twin intervention for type 2 diabetes: Aretrospective real-world study","volume":"14","author":"Shamanna","year":"2024","journal-title":"Sci. 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