Machine learning-based risk prediction models for type 2 diabetes in primary care: a scoping review
Abstract
Background: 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.
Objective: 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.
Methods: 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.
Results: The search identified 186 records, of which four studies met the inclusion criteria. The included studies were conducted in…
How strong is the evidence?
Evidence Hub reads this as a review, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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