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

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, es...

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Main Authors: Olanrewaju, Rashidah Funke, Sahari, Nur Syarafina, Aibinu, Abiodun Musa, Hakiem, Nashrul
Format: Conference or Workshop Item
Language:English
English
Published: Institute of Electrical and Electronics Engineers, Inc. 2014
Subjects:
Q Science (General)
Online Access:http://irep.iium.edu.my/43064/1/43064_Application%20of%20neural%20networks.pdf
http://irep.iium.edu.my/43064/2/43064_Application%20of%20neural%20networks_SCOPUS.pdf
http://irep.iium.edu.my/43064/
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7042180&
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spelling my.iium.irep.430642016-12-15T09:02:24Z http://irep.iium.edu.my/43064/ Application of neural networks in early detection and diagnosis of parkinson's disease Olanrewaju, Rashidah Funke Sahari, Nur Syarafina Aibinu, Abiodun Musa Hakiem, Nashrul Q Science (General) 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. Institute of Electrical and Electronics Engineers, Inc. 2014 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/43064/1/43064_Application%20of%20neural%20networks.pdf application/pdf en http://irep.iium.edu.my/43064/2/43064_Application%20of%20neural%20networks_SCOPUS.pdf 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. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7042180&
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic Q Science (General)
spellingShingle Q Science (General)
Olanrewaju, Rashidah Funke
Sahari, Nur Syarafina
Aibinu, Abiodun Musa
Hakiem, Nashrul
Application of neural networks in early detection and diagnosis of parkinson's disease
description 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.
format Conference or Workshop Item
author Olanrewaju, Rashidah Funke
Sahari, Nur Syarafina
Aibinu, Abiodun Musa
Hakiem, Nashrul
author_facet Olanrewaju, Rashidah Funke
Sahari, Nur Syarafina
Aibinu, Abiodun Musa
Hakiem, Nashrul
author_sort Olanrewaju, Rashidah Funke
title Application of neural networks in early detection and diagnosis of parkinson's disease
title_short Application of neural networks in early detection and diagnosis of parkinson's disease
title_full Application of neural networks in early detection and diagnosis of parkinson's disease
title_fullStr Application of neural networks in early detection and diagnosis of parkinson's disease
title_full_unstemmed Application of neural networks in early detection and diagnosis of parkinson's disease
title_sort application of neural networks in early detection and diagnosis of parkinson's disease
publisher Institute of Electrical and Electronics Engineers, Inc.
publishDate 2014
url http://irep.iium.edu.my/43064/1/43064_Application%20of%20neural%20networks.pdf
http://irep.iium.edu.my/43064/2/43064_Application%20of%20neural%20networks_SCOPUS.pdf
http://irep.iium.edu.my/43064/
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7042180&
_version_ 1643612309832073216
score 13.211869

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