Artificial Intelligence-Based Prediction of Progression from Gestational Diabetes to Type 2 Diabetes

Women with a history of gestational diabetes mellitus (GDM) are at elevated risk of developing type 2 diabetes mellitus (T2DM) postpartum. This study explores the use of interpretable machine-learning models to examine associations between clinical, metabolic, lifestyle factors and postpartum diabetes status among women with history of GDM. A publicly available dataset of 1,496 women with prior GDM, comprising 29 medical, anthropometric, laboratory, and lifestyle variables, was analyzed. Eight classifiers were evaluated using 5-fold stratified cross-validation, including Logistic Regression (LogR), Decision Tree, Random Forest, Support Vector Machine, Naïve Bayes, K-Nearest Neighbor, XGBoost, and Multilayer Perceptron. Model performance was assessed using accuracy, recall, precision, F1-score, specificity, and AUC. LogR yielded the best accuracy (93.0%). Feature importance analyses identified HOMA-IR, C-peptide, uric acid, family history of diabetes, and AST as the strongest correlates of diabetes status. Given the lack of temporal information on feature measurement relative to diagnosis, results should be interpreted as associative rather than predictive. These findings demonstrate the utility of interpretable machine-learning approaches for exploratory postpartum diabetes risk stratification and underscore the need for future longitudinal validation.