Data-based identification of prediction models for glucose

Diabetes mellitus is a disease that affects to hundreds of million of people worldwide. Maintaining a good control of the disease is critical to avoid severe long-term complications. One of the main problems that arise in the (semi) automatic control of diabetes, is to get a model explaining how glycemia (glucose levels in blood) varies with insulin, food intakes and other factors, fitting the characteristics of each individual or patient. In this paper we compare genetic programming techniques with a set of clsssical identification techniques: classical simple exponential smoothing, Holt's smoothing (linear, exponential and damped), classical Holt and Winters methods and auto regressive integrated moving average modelling. We consider predictions horizons of 30, 60, 90 and 120 minutes. Experimental results shows the difficulty of predicting glucose values for more than 60 minutes and the necessity of adapt GP techniques for those dynamic enviroments.