Explainable hypoglycemia prediction models through dynamic structured grammatical evolution

• Published in: Scientific reports 2024-06, Vol.14 (1), p.12591-17, Article 12591
• Main Authors: De La Cruz, Marina, Garnica, Oscar, Cervigon, Carlos, Velasco, Jose Manuel, Hidalgo, J. Ignacio
• Format: Article
• Language: English
• Subjects: 631/114; 639/705; 692/163; Algorithms; Blood; Blood glucose; Blood Glucose - metabolism; Diabetes; Diabetes Mellitus; Evolution; Forecasting; Glucose; Heart rate; Humanities and Social Sciences; Humans; Hyperglycemia; Hypoglycemia; Hypoglycemia prediction; Learning algorithms; Machine Learning; Mathematical models; Models, Theoretical; multidisciplinary; Prediction models; Rule system; Science; Science (multidisciplinary); Structured grammatical evolution
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-63187-5

Effective blood glucose management is crucial for people with diabetes to avoid acute complications. Predicting extreme values accurately and in a timely manner is of vital importance to them. People with diabetes are particularly concerned about suffering a hypoglycemia (low value) event and, moreover, that the event will be prolonged in time. It is crucial to predict hyperglycemia (high value) and hypoglycemia events that may cause health damages in the short term and potential permanent damages in the long term. This paper describes our research on predicting hypoglycemia events at 30, 60, 90, and 120 minutes using machine learning methods. We propose using structured Grammatical Evolution and dynamic structured Grammatical Evolution to produce interpretable mathematical expressions that predict a hypoglycemia event. Our proposal generates white-box models induced by a grammar based on if-then-else conditions using blood glucose, heart rate, number of steps, and burned calories as the inputs for the machine learning technique. We apply these techniques to create three types of models: individualized, cluster, and population-based. They all are then compared with the predictions of eleven machine learning techniques. We apply these techniques to a dataset of 24 real patients of the Hospital Universitario Principe de Asturias, Madrid, Spain. The resulting models, presented as if-then-else statements that incorporate numeric, relational, and logical operations between variables and constants, are inherently interpretable. The True Positive Rate and True Negative Rate metrics are above 0.90 for 30-minute predictions, 0.80 for 60 min, and 0.70 for 90 min and 120 min for the three types of models. Individualized models exhibit the best metrics, while cluster and population-based models perform similarly. Structured and dynamic structured grammatical evolution techniques perform similarly for all forecasting horizons. Regarding the comparison of different machine learning techniques, on the shorter forecasting horizons, our proposals have a high probability of winning, a probability that diminishes on the longer time horizons. Structured grammatical evolution provides advanced forecasting models that facilitate model explanation, modification, and retesting, offering flexibility for refining solutions post-creation and a deeper understanding of blood glucose behavior. These models have been integrated into the glUCModel application, designed to serve people wi