Prediction of Blood Glucose Levels And Nocturnal Hypoglycemia Using Physiological Models and Artificial Neural Networks

Blood glucose control is a burden for subjects who live with Type 1 Diabetes (T1D). Patients with T1D aim to maintain blood glucose levels into euglycemic ranges, but this is not trivial task and requires a lifelong commitment on diabetes management. Emerging technologies (e.g. continuous glucose monitoring, insulin pump, mobile applications) have permitted to track several signals related with diabetes management closely, boosting the application of various machine learning algorithm focusing to learn the behavior of blood glucose. In this work we present the application of artificial neural networks to perform two different tasks: i) creating regression models to predict blood glucose levels continuously and ii) creating classification models to predict nocturnal hypoglycemic events. Both methods are evaluated on a dataset which contains about eight weeks of data from six different patients with T1D. Numerical results indicate that ANNs are feasible to perform these tasks satisfactorily and may be considerable to assist patients on T1D diabetes management.