RST-BatMiner: A fuzzy rule miner integrating rough set feature selection and Bat optimization for detection of diabetes disease

Abstract

Fuzzy classification rules are more interpretable and cope better with pervasive uncertainty and vagueness with respect to crisp rules. Because of this fact, fuzzy classification rules are extensively used in classification and decision support systems for disease diagnosis. But, most of the rule mining techniques failed to generate accurate and comprehensive fuzzy rules. This paper presents a hybrid decision support system based on Rough Set Theory (RST) and Bat optimization Algorithm (BA) called RST-BatMiner. It consists of two stages. In the first stage, redundant features have been removed from the data set through RST-based QUICK-REDUCT approach. In the second stage, for each class BA is invoked to generate fuzzy rules by minimizing proposed fitness function. Further, an Ada-Boosting technique is applied to the rules generated by BA to increase the accuracy rate of generated fuzzy rules. Moreover, to generate comprehensive fuzzy rules, a new ≠ (not equal) operator along with = (equal) operator is introduced into BA encoding scheme. The proposed RST-BatMiner builds consolidated fuzzy ruleset by learning the rules associated with each class separately. The proposed RST-BatMiner is experimented on six bench-mark datasets namely Pima Indians Diabetes, Wisconsin Breast Cancer, Cleveland Heart disease, iris, wine and glass, in order to validate its generalization capability. These experimental results show that except for wine dataset the proposed RST-BatMiner yields high accuracy and comprehensible ruleset when compared to other state-of-the-art bio-inspired based fuzzy rule miners and Fuzzy Rule Based Classification Systems (FRBCS) in the literature. In the case of wine dataset, the proposed RST-BatMiner yields second highest accuracy along with comprehensible ruleset.