Prediction of glucose concentration in type 1 diabetic patients using support vector regression

Diabetic patients must adhere continually to a complex daily regime in order to maintain the blood glucose levels within a safe range. Many factors impact glucose variations such as diet, medication and exercise. This work presents a modeling methodology for glucose prediction in type 1 diabetic patients. The physiological processes related to diabetes (i.e. insulin absorption, gut absorption) as well as the effects of exercise on blood glucose and insulin dynamics are quantified using compartmental models. Furthermore, the method employs Support Vector Machines for Regression to provide predictions of glucose concentration. The predictive capabilities of the resulting model are evaluated using data from three type 1 diabetic patients. The Clarke´s Error Grid Analysis is used to assess the clinical utility of the proposed prediction method.