Blood glucose level prediction in type 1 diabetes: A comparative analysis of interpretable artificial intelligence approaches

This study examines the use of different interpretable Artificial Intelligence models in predicting short-term blood glucose levels in subjects with Type 1 Diabetes. The interpretability of Artificial Intelligence models is a critical concept, especially in the medical context, because it prevents the development of the so-called “black boxes” and provides decisions that are fully understandable by both patients and healthcare professionals. The final aim of this work is to integrate such fully comprehensible models within a glucose monitoring system to ensure a more transparent management of insulin therapy and an improved patient adherence. The predictive ability of the models has been assessed using a dataset containing glucose levels and heart rate variability features for certain patients selected from the open D1NAMO dataset. The prediction problem was initially approached as a multi-series regression issue and then re-evaluated as a problem of accurate classification into seven glycemic ranges. Evaluating the predictive abilities of the models in terms of correct classifications, we show that Decision Tree outperforms the other models for the analyzed subjects, achieving a weighted F1 score of 0.87 for the best run. Finally, the experiments have also shown that integrating heart rate variability features opens up the possibility of developing non-invasive monitoring systems, reducing the burden on patients and improving their quality of life. • An analysis of the ability of interpretable Artificial Intelligence models to predict blood glucose levels. • The dataset combines HRV features with glucose levels for a future perspective of non-invasive monitoring. • The prediction of the exact value of blood glucose is traced back to a classification into seven classes of glucose. • The Decision Tree algorithm outperforms all the other methods investigated, achieving the highest F1 score.