EYE-GDM: Clinically Validated, Explainable Ensemble Learning for Gestational Diabetes

As artificial intelligence (AI) advances in healthcare, its use in maternal health shows promise but faces challenges of trust due to the black-box nature of many models. Gestational diabetes mellitus (GDM), a transient yet high-risk condition, demands accurate and interpretable prediction tools. However, existing GDM prediction studies often rely on opaque models or post-hoc explanation techniques applied after training, which limits transparency and reduces their clinical applicability. This highlights an urgent need for models that unify high predictive performance with interpretability by design. This study introduces EYE-GDM, a case-specific application of our Enhanced Interpretability Ensemble (EYE) framework, designed to predict GDM risk with clinically meaningful explanations. The pipeline evaluates multiple algorithms and selects Decision Tree (DT), k-Nearest Neighbors (k-NN), and Gradient Boosting (GB) as the best-performing base learners. These are integrated with SHAP and a logistic regression (LR) meta-model to construct EYE-GDM, embedding interpretability by weighting learner outputs with LR coefficients. This yields global (population-level) and local (patient-level) explanations consistent with medical knowledge. Tested on a dataset of 3,525 pregnancies, EYE-GDM achieved strong performance (accuracy = 0.9789, AUC-ROC = 0.9981) and provided insights into risk patterns, thresholds, and feature interactions relevant to GDM. By embedding explainability within the ensemble construction, EYE-GDM achieves transparent and clinically aligned reasoning without compromising predictive performance. Thus, EYE-GDM demonstrates how explainable AI (XAI) can translate from technical innovation to practical value in maternal care, supporting earlier risk identification and more informed clinical decisions.