Grammatical Evolution-based Approach for Extracting Interpretable Glucose-Dynamics Models

The quality of life of diabetic patients can be enhanced by devising an artificial pancreas endowed with a personalized control algorithm able to regulate the insulin dosage. A fundamental step in the building of this device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized regression model able to estimate future blood glucose values on the basis of the past glucose measurements, and the knowledge of the food intake, and of the basal and injected insulin levels. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by patients suffering from Type 1 diabetes has been employed to evaluate the proposed evolutionary automatic procedure.