Application of particle swarm optimization for computer aided diagnosis of diseases

Data mining is used in order to obtain meaningful information from the data obtained in many different fields by applying several methods. Data mining is widely used to analyze medical data to make diagnosis of several diseases as this is very important topic and there exists large amount of available data in medical domain. In this study, Particle Swarm Optimization (PSO) is used to reduce the size of the medical data by making feature selection to perform better data analysis from healthcare datasets. To reach our goal, Breast Cancer Coimbra, Diabetic Retinopathy Debrecen, Self-Care Activities, and Lee Silverman Voice Treatment datasets that are used to diagnose breast cancer, diabetic retinopathy, children's self-care problems, speech disorders of patients having Parkinson disease, respectively, obtained from UCI Machine Learning Repository, are analyzed by using Naïve Bayes (NB), Support Vector Machines (SVM), and Random Forests (RF) classifiers. The experimental analysis has shown that the PSO based feature selection improves the classification accuracy of diagnosis of diseases for the NB and RF classifiers. The PSO based method is also compared with the well-known feature selectors that are information gain (IG), chi-square (CHI2) and Relief. It is observed that PSO based method has better performance than that of IG and CHI2 methods, and similar results with the Relief method.