Least-square support vector machine and wavelet selection for hearing loss identification

Abstract

Hearing loss (HL) is a kind of common illness, which can significantly reduce the quality of life. For example, HL often results in mishearing, misunderstanding, and communication problems. Therefore, it is necessary to provide early diagnosis and timely treatment for HL. This study investigated the advantages and disadvantages of three classical machine learning methods: multilayer perceptron (MLP), support vector machine (SVM), and least-square support vector machine (LS-SVM) approach and made a further optimization of the LS-SVM model via wavelet entropy. The investigation illustrated that the multilayer perceptron is a shallow neural network, while the least square support vector machine uses hinge loss function and least-square optimization method. Besides, a wavelet selection method was proposed, and we found db4 can achieve the best results. The experiments showed that the LS-SVM method can identify the hearing loss disease with an overall accuracy of three classes as 84.89 ± 1.77, which is superior to SVM and MLP. The results show that the least-square support vector machine is effective in hearing loss identification.