Transfer Learning in Hypoglycemia Classification

Patients with type 1 diabetes (T1D) have a higher risk of experiencing hypoglycemia, which is a severe condition of decreased blood glucose levels under 70 mg/dL and can result in coma, or death in the worst case. Prediction algorithms could improve diabetes care by enabling preventive actions, but research is limited by available multivariate datasets. Thus, this work investigates the feasibility of transfer learning between two different datasets of people with T1D and type 2 pre-diabetes using a 1 Dimensional Convolutional Neural Network (1DCNN) model. Moreover, different thresholds for defining hypoglycemia are compared for the pre-diabetes group. The results show that transfer learning could be feasible if the model is trained on T1D with a threshold of 70 mg/dL, while a threshold of 80–85 mg/dL achieved the best training performance for the pre-diabetes data.