Can clustering improve glucose forecasting with genetic programming models?

This study investigates how to improve the predictions of glucose values obtained with genetic programming models. A set of statistical techniques are used to discover glucose profiles that identify similar situations in patients with type 1 diabetes mellitus, and incorporate this knowledge to the models. Glucose time series are divided into 4-hour non-overlapping slots and clustered using the technique based on decision trees called chi-square automatic interaction detection, to classify glucose profiles into groups using two decision variables: day of the week and time slot of the day. The objective is to customize models for different glucose profiles that appear in the patient's day-to-day. Genetic programming models created with glucose values from the original data-set are compared to those of models created with classified glucose values. Significant differences (p-value < 0.05) and associations are observed between the glucose profiles. In general, using classified glucose values in models created with genetic programming, the accuracy of the predictions improves in comparison with those of models created with the original data-set. We concluded that the classification process can be useful to correct and improve habits or clinical therapies in patients, and obtain more accurate models through automatic learning techniques and artificial intelligence.