Predicting Glucose Levels in Patients with Type1 Diabetes Based on Physiological and Activity Data

Managing blood glucose levels for type 1 diabetes patients is an absolute necessity to better glycemic control. In this paper, we present a predictive model that uses physiological measurements and physical activity to predict continuous glucose levels and help patients reduce and prevent hyperglycemia and hypoglycemia exposure, conditions that are harmful to patient health. The data of this research includes 4 months of physiological measurements, physical activity, and nutrition information collected from 93 patients with diabetes using the Medtronic MiniMed™ 530G insulin delivery system with Enlite™ sensor. After data preprocessing, missing value imputation, feature extraction, and feature selection, a set of 180 features were derived to represent the raw data. Then, an appropriate predictive model was developed based on machine-learning algorithms to predict continuous glucose levels. The prediction accuracy and error have been calculated to evaluate the performance of the system. The results demonstrated that the predicted glucose levels closely followed the actual sensor glucose (SG) values measured by subcutaneous glucose sensor.