Analysis of CGM data in pregnant women through machine learning and mechanistic models

Abstract- Accurate glucose forecasting is crucial for optimizing diabetes management, particularly in pregnant women, where gestational diabetes mellitus (GDM) poses significant maternal and fetal risks. Continuous glucose monitoring (CGM) provides valuable real-time data, but the complexity and variability of glucose dynamics make precise prediction challenging. This study compares machine learning (ML) and mathematical modeling (MM) approaches to forecast glucose levels in non-diabetic pregnant women. We introduce a novel hybrid deep learning model (GRU-LSTM) alongside a refined mechanistic model based on stochastic differential equations. Our results indicate that mechanistic models offer the highest predictive accuracy, particularly for longer prediction horizons, while ML-based approaches provide computational efficiency.Clinical relevance - This research lays the foundation for advanced hybrid ML-MM models that enhance glucose fore-casting reliability, supporting better clinical decision-making and diabetes management during pregnancy.