SWARM OPTIMIZED FUZZY REASONING MODEL (SOFRM)

Abstract

Early diagnosis of Diabetes is important as it reduces the chances of related complications to arise. Swarm Intelligence is being widely used for medical diagnostic purposes. Many classifiers are being optimized by Swarm Intelligence techniques to reduce the cumbersome procedure of defining complex rules and frameworks. Cuckoo Search is a recently developed algorithm which uses the concept of Swarm Intelligence. Cuckoo Search mimics the brooding behavior of some Cuckoo species. Cuckoo Search is enhanced by Levy Flights which follows Levy distribution. Fuzzy Reasoning Model represents some expert knowledge via simple linguistic rules. This makes the system more understandable. Employing Fuzzy Reasoning Model to represent a system for diabetes diagnosis will help an expert who has to consider a large number of factors before making a decision. In this way human error will be minimized. Objective of this thesis is to employ Fuzzy Reasoning Model for Diabetes Diagnosis. Cuckoo Search has been used to optimize fuzzy model for better results. Pima Indians Diabetes Data set has been used to evaluate the accuracy of proposed classifier.