Enhancing Nocturnal Hypoglycemia Prediction Through Ensemble Feature Selection and Synthetic Data Augmentation

Nocturnal hypoglycemia (NH), which occurs when blood glucose levels fall below 70 mg/dL during sleep, remains a common complication in people with type 1 diabetes. Despite advances in continuous glucose monitoring (CGM), controlling both glucose and glycemic events remains challenging. Artificial Intelligence (AI) models are promising for identifying CGM patterns associated with NH and support its prediction, thereby reducing associated complications. However, two main challenges limit the performance of AI-based models for NH: (i) the class imbalance caused by the low number of NH events in the datasets; and (ii) the high-dimensionality caused by the extraction of CGM-derived features. To address this, we propose a two-stage methodology that combines ensemble feature selection (FS) methods and synthetic data augmentation techniques. We employed CGM features extracted using two window-based approaches (non-overlapping and overlapping) with three, four, and six windows. Model interpretability is provided through feature analysis using Shapley additive explanations (SHAP), enabling the identification of associated NH factors. We employed CGM data from 52 individuals with T1D, collected at Complejo Hospitalario Insular-Materno Infantil de Las Palmas de Gran Canaria, Spain. Among the evaluated configurations, the six-window configuration yielded the highest reported predictive performance. The two LR models examined in the SHAP analysis, both trained with RUS and overlapping windows, achieved test-set AUCROC values of 0.7403 using all features and 0.7400 using the selected feature subset, calculated from continuous prediction scores. Our findings extend prior evidence on temporal CGM representations to an adult NH setting not restricted to exercise-associated nights, using bootstrap-based Relief FS and alternative windowing configurations. Our interpretable methodology provides a clinically relevant approach for the early prediction of NH, supporting decision-making and enhancing the effectiveness of CGM-based monitoring systems. The proposed methodology may support timely preventive interventions and contribute to improving patient safety and confidence in diabetes management.