A comparative study of medical data classification based on LS-SVM and metaheuristics approaches

In this article, we present an application of metaheuristics optimization approaches to improve medical classifier performance. Genetic Algorithm (GA), Simulated Annealing (SA) and Particle Swarm Optimization (PSO) have been applied in conjunction with Least Square Support Vector Machine (LS-SVM) approach to optimize the total misclassification error in term of False Positive and False Negative rates. We validate our experimental results, based on five well known unbalanced medical datasets. Presented results show that the SA achieved the best results. Both SA and GA outperform PSO metaheuristic.