Generative adversarial network-based data augmentation for improving hypoglycemia prediction: A proof-of-concept study

Abstract

Background and objective: Hypoglycemia is one of the major barriers for intensive insulin treatment to achieve optimal glycemic control for people with diabetes. Accurate prediction of hypoglycemia became an important factor for advancing insulin therapy, and thus numerous studies have proposed data-driven models. However, the data-driven models still suffer from performance degradation due to severe data imbalance between hypoglycemia and non-hypoglycemia. To overcome this problem, we propose a generative adversarial network (GAN) based data augmentation method, generating realistic continuous glucose monitoring (CGM) time series labeled hypoglycemia. Methods: Having acquired a large-scale CGM time series dataset, we compared the performance of various models before and after five data augmentation methods. Results: The GAN-based data augmentation method improved the hypoglycemia prediction performance when combined with ML models and we found that the data augmentation method was comparable to conventional data augmentation method. Through visualization, it was found that successfully generated CGM time series satisfied a given condition, and the generated CGM time series were visually separated according to the given condition in an embedding space. These results suggest that GAN-based data augmentation is a promising approach for solving the severe data imbalance of hypoglycemia prediction. Conclusions: We believe that the combination of more accurate hypoglycemia prediction models and intensive insulin therapy will result in better glycemic control for people with diabetes. © 2024 Elsevier Ltd