Parkinson’s Disease Prediction Using Artificial Neural Network

Download Edit this record How to cite View on PhilPapers
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%.
PhilPapers/Archive ID
SADPDP
Upload history
Archival date: 2019-01-29
View other versions
Added to PP index
2019-01-29

Total views
672 ( #7,815 of 2,440,225 )

Recent downloads (6 months)
30 ( #23,696 of 2,440,225 )

How can I increase my downloads?

Downloads since first upload
This graph includes both downloads from PhilArchive and clicks on external links on PhilPapers.