{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:12:29Z","timestamp":1788955949135,"version":"build-2803163510"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Type 2 diabetes mellitus (T2DM) is a major global public health challenge, with many individuals remaining undiagnosed until complications develop. Machine learning (ML)-based risk prediction models have the potential to support early identification of individuals at increased risk using primary care data. However, the characteristics and applicability of these models within primary care settings have not been comprehensively mapped.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To systematically map the available evidence on machine learning (ML)-based models for risk prediction, early detection, and case-finding of type 2 diabetes in primary care, and to summarize their characteristics, including predictors, modeling approaches, validation strategies, and model performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A scoping review was conducted following the Joanna Briggs Institute methodology and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). PubMed\/MEDLINE, Scopus, and Ovid MEDLINE were searched for English-language studies published between January 2011 and December 2025. Primary research describing ML-based risk prediction models developed, validated, evaluated, or intended for implementation in primary care was eligible. Data were extracted using a structured charting form and synthesized descriptively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The search identified 186 records, of which four studies met the inclusion criteria. The included studies were conducted in Sweden, Canada, Saudi Arabia, and Hong Kong between 2024 and 2025. Three studies focused on model development and internal validation, while one externally validated previously developed non-laboratory prediction models. A range of ML approaches was identified, including stochastic gradient boosting, federated learning, multilayer perceptron, random forest, support vector classification, na\u00efve Bayes, and decision tree algorithms, with logistic regression commonly used as a comparator. Models primarily utilized routinely collected demographic, anthropometric, lifestyle, and electronic health record-derived variables. Most studies reported moderate-to-good predictive performance; however, evidence regarding external validation, calibration, and prospective implementation within routine primary care remained limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Evidence on ML-based risk prediction models for T2DM applicable to primary care remains limited despite growing interest in AI for diabetes prediction. Existing models demonstrate promising predictive performance using routinely available clinical information, but greater emphasis is needed on external validation, calibration, prospective implementation, and evaluation across diverse primary care populations before widespread clinical adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Review registration<\/jats:title>\n                    <jats:p>\n                      Open Science Framework\n                      <jats:ext-link>https:\/\/osf.io\/mbfrz<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1946070","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:57:26Z","timestamp":1788955046000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning-based risk prediction models for type 2 diabetes in primary care: a scoping review"],"prefix":"10.3389","volume":"14","author":[{"given":"Rashita","family":"Ravi","sequence":"first","affiliation":[{"name":"Department of Public Health, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jeby Jose","family":"Olickal","sequence":"additional","affiliation":[{"name":"Department of Public Health, Amrita Institute of Medical Sciences, Amrita Vishwa Vidyapeetham","place":["Kochi, Kerala, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","volume-title":"IDF Diabetes Atlas","year":"2021"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e1969","DOI":"10.1002\/hsr2.2004","article-title":"Diabetes mellitus, the fastest growing global public health concern: Early detection should be focused","volume":"7","author":"Hossain","year":"2024","journal-title":"Health Sci Rep"},{"key":"B3","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1038\/s41574-022-00690-7","article-title":"The burden and risks of emerging complications of diabetes mellitus","volume":"18","author":"Tomic","year":"2022","journal-title":"Nat Rev Endocrinol"},{"key":"B4","doi-asserted-by":"publisher","first-page":"S27","DOI":"10.2337\/dc26-S002","article-title":"Diagnosis and classification of diabetes: Standards of Care in Diabetes-\u22122026","volume":"49","year":"2026","journal-title":"Diabetes Care"},{"key":"B5","article-title":"WHO package of essential noncommunicable (PEN) disease interventions for primary health care","year":"2020"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e003126","DOI":"10.1136\/bmjph-2025-003126","article-title":"Challenges and strategies for early diagnosis and management of type 2 diabetes mellitus at the primary healthcare level in rural Western Vidarbha, India: a qualitative study","volume":"3","author":"Kawalkar","year":"2025","journal-title":"BMJ Public Health"},{"key":"B7","doi-asserted-by":"publisher","first-page":"S50","DOI":"10.2337\/dc26-S003","volume":"49","year":"2026","journal-title":"Diabetes Care"},{"key":"B8","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1038\/s41746-023-00933-5","article-title":"A scoping review of artificial intelligence-based methods for diabetes risk prediction","volume":"6","author":"Mohsen","year":"2023","journal-title":"npj Digit Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"103132","DOI":"10.1016\/j.artmed.2025.103132","article-title":"Advances in artificial intelligence for diabetes prediction: insights from a systematic literature review","volume":"162","author":"Khokhar","year":"2025","journal-title":"Artif Intell Med"},{"key":"B10","doi-asserted-by":"publisher","first-page":"100078","DOI":"10.1016\/j.lanprc.2025.100078","article-title":"Artificial intelligence in primary care: innovation at a crossroads","volume":"2","author":"Laranjo","year":"2026","journal-title":"Lancet Prim Care"},{"key":"B11","doi-asserted-by":"publisher","first-page":"e65950","DOI":"10.2196\/65950","article-title":"AI and Primary Care: Scoping Review","volume":"27","author":"Katonai","year":"2025","journal-title":"J Med Internet Res"},{"key":"B12","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1093\/ckj\/sfaa188","article-title":"External validation of prognostic models: what, why, how, when and where?","volume":"14","author":"Ramspek","year":"2021","journal-title":"Clin Kidney J"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e074820","DOI":"10.1136\/bmj-2023-074820","article-title":"Evaluation of clinical prediction models (part 2): how to undertake an external validation study","volume":"384","author":"Riley","year":"2024","journal-title":"BMJ"},{"key":"B14","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1080\/17446651.2024.2400706","article-title":"The utility of a machine learning model in identifying people at high risk of type 2 diabetes mellitus","volume":"19","author":"Alkattan","year":"2024","journal-title":"Expert Rev Endocrinol Metab"},{"key":"B15","first-page":"1099","article-title":"Federated diabetes prediction in Canadian adults using real-world cross-province primary care data","volume":"2024","author":"Tang","year":"2025","journal-title":"AMIA Annu Symp Proc"},{"key":"B16","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.pcd.2024.06.010","article-title":"A machine learning tool for identifying patients with newly diagnosed diabetes in primary care","volume":"18","author":"W\u00e4ndell","year":"2024","journal-title":"Prim Care Diabetes"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1111\/jdi.14256","article-title":"External validation of the Hong Kong Chinese non-laboratory risk models and scoring algorithm for case finding of prediabetes and diabetes mellitus in primary care","volume":"15","author":"Cheng","year":"2024","journal-title":"J Diabetes Investig"},{"key":"B18","article-title":"Clinical prediction models: a practical approach to development, validation, and updating","author":"Steyerberg","year":"2009","journal-title":"New York: Springer"},{"key":"B19","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1002\/(sici)1097-0258(20000229)19:4<453::aid-sim350>3.0.co;2-5","article-title":"What do we mean by validating a prognostic model?","volume":"19","author":"Altman","year":"2000","journal-title":"Stat Med"},{"key":"B20","first-page":"759","article-title":"A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects","volume":"53","author":"Mohan","year":"2005","journal-title":"J Assoc Physicians India"},{"key":"B21","doi-asserted-by":"publisher","first-page":"197","DOI":"10.5694\/j.1326-5377.2010.tb03478.x","article-title":"AUSDRISK: An Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures","volume":"192","author":"Chen","year":"2010","journal-title":"Med J Aust"},{"key":"B22","doi-asserted-by":"publisher","first-page":"b880","DOI":"10.1136\/bmj.b880","article-title":"Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDiabetes","volume":"338","author":"Hippisley-Cox","year":"2009","journal-title":"BMJ"},{"key":"B23","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.diabres.2005.02.016","article-title":"Derivation and validation of diabetes risk score for urban Asian Indians","volume":"70","author":"Ramachandran","year":"2005","journal-title":"Diabetes Res Clin Pract"},{"key":"B24","doi-asserted-by":"publisher","first-page":"30","DOI":"10.4103\/ijcm.IJCM_136_20","article-title":"Validation of Indian Diabetes Risk Score for screening prediabetes in West Tripura district of India","volume":"46","author":"Sengupta","year":"2021","journal-title":"Indian J Community Med"},{"key":"B25","doi-asserted-by":"publisher","first-page":"725","DOI":"10.2337\/diacare.26.3.725","article-title":"The Diabetes Risk Score: a practical tool to predict type 2 diabetes risk","volume":"26","author":"Lindstr\u00f6m","year":"2003","journal-title":"Diabetes Care"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine learning in medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"B27","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J Clin Epidemiol"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","article-title":"Machine learning in medicine","volume":"380","author":"Rajkomar","year":"2019","journal-title":"N Engl J Med"},{"key":"B29","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1016\/S0140-6736(19)30037-6","article-title":"Reporting of artificial intelligence prediction models","volume":"393","author":"Collins","year":"2019","journal-title":"Lancet"},{"key":"B30","doi-asserted-by":"publisher","first-page":"f6753","DOI":"10.1136\/bmj.f6753","article-title":"Implementation research: what it is and how to do it","volume":"347","author":"Peters","year":"2013","journal-title":"BMJ"},{"key":"B31","doi-asserted-by":"publisher","first-page":"1351","DOI":"10.1001\/jama.2019.10306","article-title":"Making machine learning models clinically useful","volume":"322","author":"Shah","year":"2019","journal-title":"JAMA"},{"key":"B32","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and Machine Learning in health care","volume":"319","author":"Beam","year":"2018","journal-title":"JAMA"},{"key":"B33","doi-asserted-by":"publisher","first-page":"e0174944","DOI":"10.1371\/journal.pone.0174944","article-title":"Can machine-learning improve cardiovascular risk prediction using routine clinical data?","volume":"12","author":"Weng","year":"2017","journal-title":"PLoS One"},{"key":"B34","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1097\/MLR.0b013e3182408812","article-title":"Effectiveness-implementation hybrid designs: combining elements of clinical effectiveness and implementation research to enhance public health impact","volume":"50","author":"Curran","year":"2012","journal-title":"Med Care"}],"container-title":["Frontiers in Public Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1946070\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:57:27Z","timestamp":1788955047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fpubh.2026.1946070\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,9]]},"references-count":34,"alternative-id":["10.3389\/fpubh.2026.1946070"],"URL":"https:\/\/doi.org\/10.3389\/fpubh.2026.1946070","relation":{},"ISSN":["2296-2565"],"issn-type":[{"value":"2296-2565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,9]]},"article-number":"1946070"}}