Genetic Programming Techniques for Glucose Prediction in People with Diabetes

Accurately predicting blood glucose levels in individuals with diabetes is essential for effectively managing and preventing complications. This paper explores the application of Grammatical Evolution, a genetic programming technique, for glucose prediction. It discusses how Grammatical Evolution has been employed in addressing various challenges related to glucose prediction, such as limited actual recorded data, prediction safety, interpretability of models, consideration of latent variables, and prognosis of hypoglycemia episodes. Building upon this research, the paper presents a comprehensive framework for glucose control that utilizes evolutionary techniques, primarily emphasizing structured grammatical evolution. The framework encompasses several stages, including data gathering, data augmentation, extraction of latent variability features, scenario clustering, structured grammatical evolution training, development of interpretable personal models, derivation of classification rules, glucose prediction, hypoglycemia alert, and glucose control. By harnessing the power of evolutionary algorithms, the framework optimizes model performance and adapts to individual patient characteristics. The proposed framework presents a promising approach to improve glucose monitoring and control, thereby contributing to better diabetes management and improved quality of life for patients.