Multilevel Fuzzy Inference System for Estimating Risk of Type 2 Diabetes

Abstract

In this paper, we present a multilevel fuzzy inference model for predicting the risk of type 2 diabetes. We have designed a system for predicting this risk by taking into account various factors such as physical, behavioral, and environmental parameters related to the investigated patient and thus facilitate experts to diagnose the risk of diabetes. The important risk parameters of type 2 diabetes are identified based on the literature and the recommendations of experts. The parameters are scaled and fuzzified on their own universe and, based on the experts’ recommendation, fuzzy inference subsystems are created with 3–4 related risk parameters to calculate the risk level. These sub-systems are then arranged into Mamdani-type inference systems so that the system calculates an aggregated risk level. The overview of the large number of diverse types of risk factors, which may be difficult for specialists and doctors, is facilitated by the proposed system.