Exploring Causal Modeling to Enhance Diabetes Prediction and Management

Gestional Diabetes Mellitus is a common pregnancy complication, affecting up to 10% of pregnancies worldwide. This study applies causal modeling to PIMA dataset to support diabetes prediction and management. Results show that healthy lifestyle interventions reduce glucose level, and counterfactual analysis suggest that modifying key-factors may prevent disease onset. Findings highlight the potential of causal inference for risk identification and prevention.