Affiliation:
1. National Institute of Technology Silchar, India
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.
Reference39 articles.
1. Metabolic profiling of Parkinson’s disease: Evidence of biomarker from gene expression analysis and rapid neural network detection.;S. S.Ahmed;Journal of Biomedical Science,2009
2. A Nonlinear Decision Tree based Classification Approach to Predict the Parkinson’s disease using Different Feature Sets of Voice Data.;S.Aich;International Conference on Advanced Communications Technology (ICACT),2018
3. Detecting of Parkinson Disease through Voice Signal Features.;Y.Alemami;The Journal of American Science,2014
4. Early Prediction of Parkinson’s Disease using Artificial Neural Network
5. Parkinson Diagnosis using Neural Network: A Survey.;S.Bhande;International Journal of Innovative Research in Science. Engineering and Technology,2013