Application of Tree Structures of Fuzzy Classifier to Diabetes Disease Diagnosis

Abstract

Abstract.Recently, computational intelligence has been successfully applied to diagnosis of many diseases. In this paper, tree structures of fuzzy classifier have been applied to diagnosis of diabetes disease that is very common and important disease. Tree structures of fuzzy classifier can reduce the size of rules by optimally placing fuzzy neurons as nodes and selecting relevant input subspaces as leaves. In the optimization stage, we considered two-step optimization where genetic algorithm (GA) develops the binary structure by optimally selecting the nodes and leaves, and then random signal-based learning (RSL) further refines the binary connections. We considered the diabetes disease dataset obtained from the UCI Machine Learning Repository Database.