An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models

Abstract

Within this paper a Grammatical Evolution al-gorithm is exploited to induce personalized and interpretable glucose forecasting models for diabetic patients based on the historical measurements of the glucose, the carbohydrates, and the injected insulin. A real-world data set of Type 1 diabetic patients is used to assess the induced models. The experimental trials show that the performance of extracted models is compara-ble with that obtained by other state-of-the-art techniques that require a more significant computational effort.