Detection of erythemato-squamous diseases using AR-CatfishBPSO-KSVM

Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In thiswork, we introduces a diagnosis model based on Catfish binary particle swarm optimization(CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our featureselection method to diagnose erythemato-squamous diseases. The proposed model consisted of two stages.In the first stage, AR is used to select the optimal feature subset from the original feature set. Next, basedon the fact that kernel parameter setting in the SVM training procedure significantly influences theclassification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO basedapproach is employed for parameter determination of KSVM. Experimental results show that the proposedAR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of theerythemato-squamous disease dataset which shows that our proposed method is more accurate comparedto other popular methods in this literature like Support vector machines and AR-MLP (association rules -multilayer perceptron). It should be mentioned that we took our dataset from University of CaliforniaIrvine machine learning database.