A Grammatical Evolution Approach for Estimating Blood Glucose Levels

The management of diabetes is a very complex task, hence devising automatic procedures able to predict the glycemic level can represent a significant step towards the building of an artificial pancreas capable of providing the needed amounts of insulin boluses. This paper presents a Grammatical Evolution-based algorithm aiming at extrapolating a regression model able to estimate the blood glucose level in future instants of time through interstitial glucose measurements. The hypothesis is that the amounts of carbohydrates assumed, of basal insulin levels and of those administered with boluses are known. Experiments, performed on a real-world database made up of five patients suffering from Type 1 diabetes, are shown in terms of Clark Error Grid analysis. To evaluate the effectiveness of the predictions derived from the proposed approach, the results obtained are compared against those obtained by other state-of-the-art evolutionary-based methods very recently proposed.