Within this paper a Grammatical Evolution algorithm 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 comparable with that obtained by other state-of-the-art techniques that require a more significant computational effort.
An Evolution-based Machine Learning Approach for Inducing Glucose Prediction Models
Ivanoe De Falco;Umberto Scafuri;Ernesto Tarantino;
2022
Abstract
Within this paper a Grammatical Evolution algorithm 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 comparable with that obtained by other state-of-the-art techniques that require a more significant computational effort.File in questo prodotto:
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