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Machine learning models for identifying prediabetes: a systematic review of performance, validation, and implications
Liang Wang1, Yan Zhang1, Hao Yang2
1Department of Geriatric Endocrinology, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
Background:
Machine learning models have been proposed for identifying prediabetes, but their methodological quality, generalisability, and readiness for primary care implementation remain uncertain.
Methods:
We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant systematic review registered in PROSPERO (CRD420251029165). Eight databases were searched from inception to 1 April 2025, with PubMed, Embase, and Web of Science Core Collection updated through 1 August 2026. The primary review question focused on current/prevalent prediabetes identification; longitudinal/prognostic studies addressing future dysglycaemia were retained only as supplementary evidence and interpreted separately. Principal performance and implementation conclusions were restricted to current/prevalent identification. Two reviewers independently screened studies and extracted data. Risk of bias and applicability were assessed using Prediction Model Risk of Bias Assessment Tool (PROBAST). Findings were synthesised descriptively because of heterogeneity.
Results:
Twenty-six studies involving 435,647 participants were included. Of these, 23 studies constituted the primary evidence base for current/prevalent prediabetes identification, whereas three longitudinal/prognostic studies were retained as supplementary evidence and interpreted separately. Across all included evidence, 110 models were reported. Development or internal validation area under the receiver operating characteristic curves (AUCs) ranged from 0.49 to 0.9989 across heterogeneous modelling tasks; the highest value represented overall multiclass discrimination rather than a prediabetes-specific binary AUC. Independent external validation was reported in only one study (1/26, 3.8%), in which the area under the receiver operating characteristic curve (AUROC) for prediabetes decreased from 0.746 in internal evaluation to 0.590 and 0.523 in two external cohorts. Fourteen studies were judged to have a low overall risk of bias, whereas 12 studies (46.2%) were judged to have a high overall risk of bias. Calibration, clinical utility assessment, and subgroup fairness evaluation remained limited.
Conclusion:
Machine learning models show potential to support current/prevalent prediabetes identification in primary care, particularly when based on routinely collected variables. However, strong performance observed during model development or internal validation should not be interpreted as evidence of implementation readiness. Given the limited independent external validation and insufficient evidence regarding calibration, clinical utility, fairness, and real-world workflow integration, current evidence remains insufficient to support routine primary care implementation. These conclusions relate to current/prevalent prediabetes identification and should not be extrapolated to longitudinal/prognostic prediction of future prediabetes, impaired fasting glucose, or related dysglycaemic progression.
Systematic Review Registration:
PROSPERO CRD420251029165.
