Diagnosis of Parkinson’s Disease using Principal Component Analysis and Boosting Committee Machines

Parkinson’s disease (PD) has become one of the most common degenerative disorders of the central nervous system. In this study, our main goal was to discriminate between healthy people and people with Parkinson’s disease. In order to achieve this we used artificial neural networks, and dataset taken from University of California, Irvine machine learning database, having 48 normal and 147 PD cases. We examine the performance of neural network systems with back propagation together with a majority voting scheme. In order to train examples we used boosting by filtering technique with seven committee machines, and principal component analysis is used for data reduction. The experimental results have demonstrated that the combination of these proposed methods has obtained very good results with correct positive value of 92% on the classification of PD.