Background and Aims
This research is motivated by the challenge of creating a general method to accurately predicting future glucose levels, so, an automated or manual system can decide when and how much insulin to inject in order to maintain glucose levels within a healthy range. It is imperative to avoid predictions that may trigger unnecessary treatments or, even worse, treatments that are harmful to the patient.
The proposed methodology is a three-step process: Data collection, preprocessing, and division, Data clustering and detection of a set of glucose profiles, and, Models training, creating models by evolutionary computation. CHAID (CHi-square Automatic Interaction Detection) is applied to recursively divide the data in relation to a target variable using multiple divisions between the different input variables: days of the week and time slots. A data augmentation algorithm hat generates synthetic glucose time series is used in training datasets to develop meaningful information and significantly enhance data quality. Next, models based on Genetic Programming (GP) are created using cross-validation. Then, the best model of 10 repetitions is selected by the Akaike Information Criterion (AIC).
Data was collected from ten patients. The predictors used in the construction of the tree are the day of the week and the time slot. Significant differences were observed in the glucose profiles classified for each of those categories.
The accuracy of predictions with models created with GP is better for shorter time horizons and gradually gets worse as the time horizon increases from 30 to 240 minutes.
Thanks: RTI2018-095180-B-I00 and Fundación Eugenio Rodriguez Pascual