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

Mohammed, Farhan
He, Xiangjian
Lin, Yiguang
Chen, Jinjun
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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%.
Big Data on Healthcare Application, Information Technology in Healthcare, Big data, classification, feature selection, healthcare, Parkinson disease
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