Classification of Parkinson's disease by using voice measurements

Abstract

In this study, a new approach has been presented to classify Parkinson's disease (PD). In order to discriminate healthy people from the PD patients, several measurements extracted from sound samples of 31 people, 23 with PD, have been applied to four different classifiers. In order to classify the subject as PD patient or healthy, a probabilistic neural network (PNN), a generalised regression neural network (GRNN), a support vector machine and a k-nearest neighbour have been carried out. Half of the dataset are used for training, remaining data are used for testing in order to determine the performance of the classifiers. In each classification process two-fold cross validation method is utilised to determine which subset represents the entire dataset. It is shown that reasonable results can be obtained by following the proposed methods.