A Novel Model for Classification of Parkinson’s Disease: Accurately Identifying Patients for Surgical Therapy

dc.contributor.author Mohammed, Farhan
dc.contributor.author He, Xiangjian
dc.contributor.author Lin, Yiguang
dc.contributor.author Chen, Jinjun
dc.date.accessioned 2019-01-03T00:19:01Z
dc.date.available 2019-01-03T00:19:01Z
dc.date.issued 2019-01-08
dc.description.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 Naïve 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%.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.452
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59810
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Big Data on Healthcare Application
dc.subject Information Technology in Healthcare
dc.subject Big data, classification, feature selection, healthcare, Parkinson disease
dc.title A Novel Model for Classification of Parkinson’s Disease: Accurately Identifying Patients for Surgical Therapy
dc.type Conference Paper
dc.type.dcmi Text
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