Application of neural networks in early detection and diagnosis of parkinson's disease

Olanrewaju, Rashidah Funke and Sahari, Nur Syarafina and Aibinu, Abiodun Musa and Hakiem, Nashrul (2014) Application of neural networks in early detection and diagnosis of parkinson's disease. In: 2014 International Conference on Cyber and IT Service Management (CITSM), 3rd-6th Nov. 2014, Jakarta, Indonesia.

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Abstract

Parkinson’s disease (PD) is a chronic neurological progressive disorder caused by lack of the chemical dopamine in the brain. Up to today, there is still no cure or prevention for PD, and usually the disease worsens gradually over time. However, this disease can be controlled with some treatment, especially in the early stage. Hence, this study proposes a method in early detection and diagnosis of PD by using the Multilayer Feedforward Neural Network (MLFNN) with Back-propagation (BP) algorithm. This MLFNN with BP algorithm is simulated using MATLAB software. The dataset information used in this study was taken from the Oxford Parkinson’s Disease Detection Dataset. The output of the network is classified into healthy or PD by using K-Means Clustering algorithm. The performance of this classifier was evaluated based on the three parameters; sensitivity, specificity and accuracy. The result shows that network can be used in diagnosis and detection of PD due to the good performance, which is 83.3% for sensitivity, 63.6% for specificity, and 80% for accuracy.

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	6796/43064
Uncontrolled Keywords:	Accuracy, Diseases, Sensitivity, Neurons, Neural networks, Classification algorithms, Clustering algorithms
Subjects:	Q Science > Q Science (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Rashidah Funke Olanrewaju
Date Deposited:	04 Jun 2015 16:06
Last Modified:	15 Dec 2016 17:02
URI:	http://irep.iium.edu.my/id/eprint/43064

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