sensors Article Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only Francesco Prendin, Simone Del Favero * , Martina Vettoretti, Giovanni Sparacino and Andrea Facchinetti Department of Information Engineering, University of Padova, 35131 Padova, Italy;
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[email protected] Abstract: In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different Citation: Prendin, F.; Del Favero, S.; experimental protocols, making a comparison of their relative merits difficult. The aim of the Vettoretti, M.; Sparacino, G.; present work was to perform a head-to-head comparison of thirty different linear and nonlinear Facchinetti, A. Forecasting of Glucose predictive algorithms using the same dataset, given