Glucose forecasting combining Markov chain based enrichment of data, random grammatical evolution and Bagging

Abstract

Diabetes Mellitus is a disease affecting more and more people every year. Depending on the kind of diabetes and sometimes on the stage of the illness, diabetic patients have to inject some amount of artificial insulin, namely bolus, before the meals, to make up the absence or malfunctioning of their natural insulin. This decision is a difficult task since they need to estimate the number of carbohydrates they are going to ingest, take into account the past and future circumstances, know the past values of glucose, evaluate if the effect of previously injected insulin has already finished and any other relevant information. In this paper, we present and compare a set of methodologies to automate the decision of the insulin bolus, which reduces the number of dangerous predictions. We combine two different data enrichment techniques based on Markov chains with grammatical evolution engines to generate models of blood glucose, and univariate marginal distribution algorithms and bagging techniques to select the set of models to assemble. In particular, we propose the Random-GE procedure, an adaptation of Random Forests to Grammatical Evolution, which leads to excellent prediction models, with a simple configuration and a reduced execution time. The ensemble gives the prediction of glucose for a duple of food and insulins, helping patients in the selection of the appropriate bolus to maintain healthy glucose levels after the meals. Experimental results show that our models get more accurate and robust predictions than previous approaches.