Data-Driven Model for Long-Term Prediction of Blood Glucose in Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is a disease that affects more than 380 million people worldwide. In this study, we developed a model, for these type of patients, that predicts blood glucose values over long prediction horizons (PHs), whose existence in the literature is almost nonexistent. These horizons allow patients to be warned in advance so that they can take action to avoid dangerous health situations. We used data from 3 of the 10 real patients available to test the implemented models. The overall results for the best model (simple Recurrent Neural Network) were: 34.82 mg/dL for root mean square error (RMSE) and 18.33% for mean absolute percentage error (MAPE) (PH = 2h); 46.59 mg/dL for RMSE and 24.35% for MAPE (PH = 4h).