Neuromuscular disease diagnosis of SVM, K-NN and DA algorithm based classification part-II

Abstract

This study includes a classification structure consisting of second part for the automatic diagnosis of the neuromuscular disease of ALS (Amyotrophic Lateral Sclerosis) and myopathy being a muscular disease. In this study feature vectors containing time domain parameters, frequency domain parameters (a total of 25 feature vectors) as well as feature vectors composed of combination of these parameters were used. In the classification stage, Support Vector Machines (SVM), K-Nearest Neighbors (K-NN) and Discriminant Analysis (DA) algorithms were employed. Experimental results showed that the multiple feature vectors proved to be more successful compared to the individual feature vectors. It is understood with this study; the classification performance depends highly on separability of feature vectors between different classes.