A hybrid kernel principal component analysis and support vector machine model for analysing sonographic features of parotid glands in Sjogren's syndrome

Sjogren's syndrome is a chronic inflammatory process that primarily involves the exocrine glands. Clinical manifestations range from autoimmune exocrinopathy to extraglandular involvement affecting the lungs, kidneys, blood vessels and muscles; Sjogren's syndrome can occur along with, or accompany, other autoimmune diseases. Moreover, diagnosing Sjogren's syndrome saves lives. The term support vector machine (SVM) refers to an emerging machine learning technique based on statistical learning theory which can solve classification problems using small sampling, non-linearity and high dimensions. However, both dimension reduction and parameter determination greatly influence the performance of the SVM technique. Therefore, this study develops a SVM with an immune algorithm (IA) and kernel principal component analysis (KSVMIA) model to diagnose Sjogren's syndrome effectively and efficiently. In the proposed KSVMIA model, the kernel-based principal component analysis (KPCA) technique is used to reduce the dimension of Sjogren's syndrome data; the IA is employed to determine the SVM models. Experimental results reveal that the developed model can classify Sjogren's syndrome data obtained from ultrasound in terms of efficiency and accuracy, showing that the presented KSVMIA model is a promising alternative for diagnosing Sjogren's syndrome.