Reducing high-risk glucose forecasting errors by evolving interpretable models for Type 1 diabetes

Diabetes mellitus is a metabolic disease involving high blood glucose levels that can lead to serious medical consequences. Hence, for diabetic patients the prediction of future glucose levels is essential in the management of the disease. Most of the forecasting approaches in the literature evaluate the effectiveness of glucose predictors only with numerical metrics. These approaches are limited because they evenly treat all the errors without considering their different clinical impact that could involve lethal effects in dangerous situations such as hypo- or hyperglycemia. To overcome such a limitation, this paper aims to devise models for reducing high-risk glucose forecasting errors for Type 1 diabetic patients. For this purpose, we exploit a Grammatical Evolution algorithm to induce personalized and interpretable forecasting glucose models assessed with a novel, composite metric to satisfy both clinical and numerical requirements of the estimated predictions. To assess the effectiveness of the proposed approach, a real-world data set widely used in literature, consisting of data from several patients suffering from Type 1 diabetes, has been adopted. The experimental findings show that the induced models are interpretable and capable of assuring predictions with a good tradeoff between medical quality and numerical accuracy and with remarkable performance in reducing high-risk glucose forecasting errors. Furthermore, their performance is better than or comparable to that of other state-of-the-art methods.