Abstract
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.
Highlights
The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology
There are many popular methods for dealing with this problem such as: principle component analysis (PCA) [9], linear discriminant analysis (LDA) [10], forward feature selection (FFS) and backward feature selection (BFS) [11,12], Individual feature selection (IFS) [13], association rules [14] and etc. based on the result reported by [7], we applied AR method to reduce the dimension of erythemato-squamous diseases dataset
The classifiers proposed for clinical decision-making were implemented by using the MATLAB software package (MATLAB version 7.2 with neural networks toolbox)
Summary
The differential diagnosis of erythemato-squamous diseases is a real challenge in dermatology. In [4] the author showed that SVM has great performance since it can handle a nonlinear classification effectively by mapping samples from low dimensional input space into high dimensional feature space with a nonlinear kernel function. She compared SVM with recurrent neural network (RNN) and multilayer perceptrons (MLPs) and showed SVM is more robust and more accurate than other methods. In [7], authors compared AR method with other feature selection algorithms As a result, they could show that using 24 features, we can achieve high accuracy rate in our classification problem.
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