Nowadays, one of the most important usages of machine learning is diagnosis of diverse diseases. In this work, we introduces a diagnosis model based on Catfish binary particle swarm optimization (CatfishBPSO), kernelized support vector machines (KSVM) and association rules (AR) as our feature selection 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, based on the fact that kernel parameter setting in the SVM training procedure significantly influences the classification accuracy and CatfishBPSO is a promising tool for global searching, a CatfishBPSO based approach is employed for parameter determination of KSVM. Experimental results show that the proposed AR-CatfishBPSO-KSVM model achieves 99.09% classification accuracy using 24 features of the erythemato-squamous disease dataset which shows that our proposed method is more accurate compared to 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 California Irvine machine learning database.