Machine Learning for Predicting the Transition From Gestational Diabetes to Type 2 Diabetes: A Systematic Review

Gestational diabetes mellitus (GDM) significantly increases the risk of developing type 2 diabetes (T2D) postpartum. Early identification of high-risk women using machine learning (ML) models could enable targeted interventions and improve outcomes. This systematic review aims to evaluate the performance, predictive features, and methodological quality of ML models designed to predict the transition from GDM to T2D. A comprehensive search was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science, yielding 178 records. After removing duplicates and screening for eligibility, 13 studies were included. Data on study characteristics, ML algorithms, predictive features, model performance, and validation methods were extracted. Risk of bias was assessed using the PROBAST (Prediction model Risk of Bias Assessment Tool). The included studies demonstrated variable performance, with area under the curve (AUC) values ranging from 0.72 to 0.92. Models incorporating omics data outperformed clinical-only models. Key predictive features included age, b ody m ass i ndex (BMI), glycemic measures, and pregnancy-specific factors. However, only 38% of studies employed robust external validation, and small sample sizes limited generalizability in some cases. Risk of bias assessment revealed low overall bias, though analytical validation methods were often unclear or insufficient. ML models, particularly those integrating omics data, show strong potential for predicting T2D risk in women with prior GDM. However, heterogeneity in validation methods and limited external validation highlight the need for standardized reporting and larger, diverse cohorts to enhance clinical applicability. Future research should focus on developing reproducible, generalizable models to guide personalized prevention strategies.