Neural network and neuro-fuzzy systems for improving diabetes therapy

Abstract

Expert management of diabetes mellitus, through good glycaemic control, is necessary development of serious short-term complications, due to the persistence of either low or high blood glucose levels (BGLs), respectively. In this paper, the use of a recurrent artificial neural network (ANN) is described which is able to predict BGL for a specific patient. This predicted BGL may then be used in a neuro-fuzzy expert system to offer short-term therapeutic advice regarding the patient's diet, exercise and insulin regime (for insulin-dependent or Type 1 diabetics). ANN training requirements are discussed, and BGL predictions for two Type 1 diabetic patients are compared with actual BGL measurements.