Authors: Bulent Bolat, Suna Bolat Sert
Addresses: Department of Electronics and Telecommunications Engineering, Yildiz Technical University, Besiktas, Istanbul, Turkey. ' Department of Electrical Engineering, Istanbul Technical University, Maslak, Istanbul, Turkey
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.
Keywords: Parkinson|s disease; probabilistic neural networks; PNN; generalised regression neural networks; GRNN; support vector machines; SVM; biomedical DSS; decision support systems; disease classification; patient classification; voice measurement; sound samples; k-nearest neighbour.
International Journal of Reasoning-based Intelligent Systems, 2010 Vol.2 No.3/4, pp.279 - 284
Published online: 12 Nov 2010 *Full-text access for editors Access for subscribers Purchase this article Comment on this article