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Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach

Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach

Debashree Devi, Saroj K. Biswas, Biswajit Purkayastha
Copyright: © 2021 |Pages: 34
ISBN13: 9781799834410|ISBN10: 1799834417|EISBN13: 9781799834427
DOI: 10.4018/978-1-7998-3441-0.ch016
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MLA

Devi, Debashree, et al. "Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach." Research Anthology on Diagnosing and Treating Neurocognitive Disorders, edited by Information Resources Management Association, IGI Global, 2021, pp. 295-328. https://doi.org/10.4018/978-1-7998-3441-0.ch016

APA

Devi, D., Biswas, S. K., & Purkayastha, B. (2021). Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach. In I. Management Association (Ed.), Research Anthology on Diagnosing and Treating Neurocognitive Disorders (pp. 295-328). IGI Global. https://doi.org/10.4018/978-1-7998-3441-0.ch016

Chicago

Devi, Debashree, Saroj K. Biswas, and Biswajit Purkayastha. "Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach." In Research Anthology on Diagnosing and Treating Neurocognitive Disorders, edited by Information Resources Management Association, 295-328. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-3441-0.ch016

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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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