Parkinson disease detection using ensemble method in PASW benchmark

Abstract

We present an ensemble method to classify Parkinson patients and healthy people. C&R Tree, Bayes Net and C5.0 are used to generate ensemble method. Using supervised learning technique, the proposed method generates rules to distinguish Parkinson patients from healthy people. The proposed method uses single classifier to generate rules which are used as input for the next used classifier and in this way final rules are generated to predict more accurate results than individual classifier used to generate ensemble method. This method shows lower number of misclassification instances than single classifiers used to build model. Ensemble method shows better results for training and testing accuracy than single classifier.