Parkinson’s Disease Prediction Using Artificial Neural Network

International Journal of Academic Health and Medical Research (IJAHMR) 3 (1):1-8 (2019)
  Copy   BIBTEX

Abstract

Parkinson's Disease (PD) is a long-term degenerative disorder of the central nervous system that mainly affects the motor system. The symptoms generally come on slowly over time. Early in the disease, the most obvious are shaking, rigidity, slowness of movement, and difficulty with walking. Doctors do not know what causes it and finds difficulty in early diagnosing the presence of Parkinson’s disease. An artificial neural network system with back propagation algorithm is presented in this paper for helping doctors in identifying PD. Previous research with regards to predict the presence of the PD has shown accuracy rates up to 93% [1]; however, accuracy of prediction for small classes is reduced. The proposed design of the neural network system causes a significant increase of robustness. It is also has shown that networks recognition rates reached 100%.

Author's Profile

Samy S. Abu-Naser
North Dakota State University (PhD)

Analytics

Added to PP
2019-01-29

Downloads
1,394 (#10,205)

6 months
226 (#10,096)

Historical graph of downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.
How can I increase my downloads?