Modelling glycaemia of diabetics : An application

We present and study, in this paper, a Tagaki-Sugeno(TS) fuzzy model that consists in a family of linear models mixed together with nonlinear membership functions. We apply this method to the problem of treatment of diabetics. Taking the insulin infusion rate as the input and the blood glucose rate as the output, we consider the patient as a black box [12], [11], whose model has to be obtained from the available measures of inputs-outputs. We dispose of a glycaemia file automatically produced for every person, and an insulin file shared by several persons. We investigate the model´s quality on two criteria: a convergence measure and the impact of datum plane covering on the outcome of a fuzzy inference system. We emphasize on the importance of datum plane covering. Many papers propose fuzzy algorithms for extraction of knowledge from numerical data [7], [8], [9]. But few works have been developed for design of experiments and datum plane covering. We propose a measure used to pre-validate a fuzzy model. This pre-validation takes place after design of the inference system. So, when the model is not pre-validated, we do not have to carry out the next steps, optimisation and validation.