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Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach
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Author(s): Debashree Devi (National Institute of Technology Silchar, India), Saroj K. Biswas (National Institute of Technology Silchar, India)and Biswajit Purkayastha (National Institute of Technology Silchar, India)
Copyright: 2019
Pages: 35
Source title:
Early Detection of Neurological Disorders Using Machine Learning Systems
Source Author(s)/Editor(s): Sudip Paul (North-Eastern Hill University Shillong, India), Pallab Bhattacharya (National Institute of Pharmaceutical Education and Research (NIPER) Ahmedabad, India)and Arindam Bit (National Institute of Technology Raipur, India)
DOI: 10.4018/978-1-5225-8567-1.ch005
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Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that occurs due to corrosion of the substantia nigra, located in the thalamic region of the human brain, and is responsible for transmission of neural signals throughout the human body by means of a brain chemical, termed as “dopamine.” Diagnosis of PD is difficult, as it is often affected by the characteristics of the medical data of the patients, which include presence of various indicators, imbalance cases of patients' data records, similar cases of healthy/affected persons, etc. Through this chapter, an intelligent diagnostic system is proposed by integrating one-class SVM, extreme learning machine, and data preprocessing technique. The proposed diagnostic model is validated with six existing techniques and four learning models. The experimental results prove the combination of proposed method with ELM learning model to be highly effective in case of early detection of Parkinson's disease, even in presence of underlying data issues.
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