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

Abstract

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 supporting its prediction, thus reducing its occurrence and complications. However, two main challenges limit the performance of AI models for NH: (i) the class imbalance due to the low number of NH events in data; 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. We performed CGM-derived feature extraction by using a time-window approach with overlapping and non-overlapping approaches. Model interpretability is provided through feature analysis using Shapley additive explanations, 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 temporal configurations evaluated, the 6-hour window yielded the highest predictive performance. With ensemble FS retaining the top 40% of features, the best-performing model achieved an Area Under the Receiver Operating Characteristic Curve of 0.68 using a logistic regression and overlapping windows. Our findings highlighted the importance of window-based extraction in improving predictive performance. 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, reducing the occurrence of NH while improving patient safety and confidence in diabetes management.