Classification of Parkinson’s disease – A comparison between Support Vector Machines and neural networks

Abstract

Parkinson's disease (PD) is a chronic and progressive movement disorder, meaning that symptoms continue and worsen over time. The diagnosis of Parkinson is challenging because currently none of the clinical tests have been proven to help in diagnosis. In this paper, the main purpose was to classify the PD people (sick) and non-PD people (healthy). Recently the machine learning methods based diagnosis of medical diseases has taken a great deal of attention. The Support Vector Machine (SVM) and the Neural Network (NN) learning methods are used as base classifiers. The support vector machine is a novel type of learning machine, based on statistical learning theory, which contains radial basis function (RBF) as special cases. 100% / 80% accuracies are reported.