Machine learning‐based prediction model for adverse pregnancy outcomes in women with gestational diabetes mellitus

AIMS/INTRODUCTION: Gestational diabetes mellitus (GDM) is one of the most frequent pregnancy complications. Investigating clinical risk factors for adverse pregnancy outcomes in women with GDM would help predict and prevent neonatal complications. We developed a machine learning model to discover risk factors for adverse pregnancy outcomes. MATERIALS AND METHODS: Women with GDM from tertiary hospitals in Korea were included (n = 305, discovery cohort; n = 911, validation cohort). Supervised machine learning classification models, including ExtraTree, RandomForest, GradientBoosting, AdaBoost, Bagging, XGBoost, and Light Gradient Boosting Machine (LGBM), were developed to predict adverse pregnancy outcomes. Outcomes included large for gestational age (LGA), small for gestational age (SGA), low Apgar score, and preterm delivery. The top-ranked risk factors identified through feature importance were further validated using binary logistic regression analysis. RESULTS: In predicting LGA, the RandomForest model achieved the highest AUROC of 0.726 on the validation cohort. For SGA, the RandomForest model achieved the highest AUROC of 0.628. For low Apgar score and preterm delivery, the ExtraTree model showed the best performance with AUROCs of 0.689 and 0.616, respectively. CONCLUSIONS: This study presents machine learning models as a foundational tool for identifying factors associated with adverse pregnancy outcomes in women with GDM. These models may serve as a foundation for future development of clinical decision support tools.