Performance study of different metaheuristics for diabetes diagnosis

Abstract

The problem of medical data classification involves an optimization phase that may be solved through metaheuristic approaches. In this work, we evaluate the performance in diagnosis of diabetes disease, using Particle Swarm Optimization (PSO), Firefly (FF) and Homogeneity-Based Algorithm (HBA) metaheuristics in conjunction with fuzzy system. Here, the fitness function in the optimization process is the total misclassification cost that is in term of false positive, false negative and unclassifiable rates. The results prove that HBA approach achieves better results than the other metaheuristics. With execution time, FF was faster than the PSO and HBA methods.