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/ http://irep.iium.edu.my/43064/ 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

id	iium-43064
recordtype	eprints
spelling	iium-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 PeerReviewed 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&
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
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/ http://irep.iium.edu.my/43064/ 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
first_indexed	2023-09-18T21:01:20Z
last_indexed	2023-09-18T21:01:20Z
_version_	1777410643752648704

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

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