One-class SVM is a classification model which provides to detect the novel instances, within a region of definite range. It is also known as SVM, as its performance is influenced by the parameter, .
Published in Chapter:
Early Detection of Parkinson's Disease: An Intelligent Diagnostic Approach
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
DOI: 10.4018/978-1-5225-8567-1.ch005
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.