@inproceedings{53b2d23e4f654b069b24e5f49e677bb4,
title = "A novel model for classification of Parkinson's disease: Accurately identifying patients for surgical therapy",
abstract = "Parkinson's disease (PD) is a neurodegenerative disorder and a global health problem that has no curative therapies. Surgery is a well-established therapy for controlling symptoms of advanced PD patients. This paper proposes a streamlined model to classify PD and to identify appropriate patients for surgical therapy. The data was gathered from the Parkinson's Progressive Markers Initiative consisting of 1080 subjects. Multilayer Perceptron (MLP), Decision trees, Support Vector Machine and Na{\"i}ve Bayes are used as classifiers. MLP achieves the highest accuracy as compared to other three classifiers. The dataset used in our experiments is from the Parkinson Progressive Markers Initiative. With feature selection, it is observed that the same classification accuracy is achieved with 60% of the attributes as that using all attributes. It is demonstrated that our classification model for PD patients produces the most accurate results and achieves the highest accuracy of 98.13%.",
author = "Farhan Mohammed and Xiangjian He and Jinjun Chen and Yiguang Lin",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE Computer Society. All rights reserved.; 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 ; Conference date: 08-01-2019 Through 11-01-2019",
year = "2019",
language = "English",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "3741--3750",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019",
address = "United States",
}