Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]

Parkinson’s disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer’s disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep...

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Main Authors:	Abu Bakar, Zahari, Ibrahim, Nur Farahiah, Ispawi, Dzufi Iszura, Md. Tahir, Nooritawati
Format:	Thesis
Language:	English
Published:	2012
Subjects:	Medicine and disease in relation to psychology Terminal care Dying Medical care Diseases of the brain
Online Access:	https://ir.uitm.edu.my/id/eprint/42965/1/42965.pdf
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id	my-uitm-ir.42965
record_format	uketd_dc
spelling	my-uitm-ir.429652021-03-08T02:55:32Z Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.] 2012-12 Abu Bakar, Zahari Ibrahim, Nur Farahiah Ispawi, Dzufi Iszura Md. Tahir, Nooritawati Medicine and disease in relation to psychology. Terminal care. Dying Medical care Neurology. Diseases of the nervous system. Including speech disorders Diseases of the brain Parkinson’s disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer’s disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD with Analysis of Variance (ANOVA) as a feature selection. The dataset information of this project has been taken from the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of above 90% before and after feature selection whilst SSG attained above 85% subsequent to implementation of ANOVA as feature selection. 2012-12 Thesis https://ir.uitm.edu.my/id/eprint/42965/ https://ir.uitm.edu.my/id/eprint/42965/1/42965.pdf text en public masters Universiti Teknologi MARA Cawangan Sarawak Faculty of Electrical Engineering
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Medicine and disease in relation to psychology Terminal care Dying Medical care Medicine and disease in relation to psychology Terminal care Dying Diseases of the brain
spellingShingle	Medicine and disease in relation to psychology Terminal care Dying Medical care Medicine and disease in relation to psychology Terminal care Dying Diseases of the brain Abu Bakar, Zahari Ibrahim, Nur Farahiah Ispawi, Dzufi Iszura Md. Tahir, Nooritawati Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
description	Parkinson’s disease (PD) is the second commonest late life neurodegenerative disease after Alzheimer’s disease. It is prevalent throughout the world and predominantly affects patients above 60 years old. It is caused by progressive degeneration of dopamine containing cells (neurons) within the deep structures of the brain called the basal ganglia and substantia nigra. Therefore, accurate prediction of PD need to be done in order to assist medical or bio-informatics practitioners for initial diagnose of PD based on variety of test results. This paper described the analysis conducted based on two training algorithms namely Levenberg-Marquardt (LM) and Scaled Conjugate Gradient (SCG) of Multilayer Perceptrons (MLPs) Neural Network in diagnosing PD with Analysis of Variance (ANOVA) as a feature selection. The dataset information of this project has been taken from the Parkinson Disease Data Set. Results attained confirmed that the LM performed well with accuracy rate of above 90% before and after feature selection whilst SSG attained above 85% subsequent to implementation of ANOVA as feature selection.
format	Thesis
qualification_level	Master's degree
author	Abu Bakar, Zahari Ibrahim, Nur Farahiah Ispawi, Dzufi Iszura Md. Tahir, Nooritawati
author_facet	Abu Bakar, Zahari Ibrahim, Nur Farahiah Ispawi, Dzufi Iszura Md. Tahir, Nooritawati
author_sort	Abu Bakar, Zahari
title	Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
title_short	Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
title_full	Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
title_fullStr	Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
title_full_unstemmed	Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]
title_sort	classification of parkinson's disease (pd) based on multilayer perceptrons (mlps) neural network and anova as a feature extraction / zahari abu bakar ... [et al.]
granting_institution	Universiti Teknologi MARA Cawangan Sarawak
granting_department	Faculty of Electrical Engineering
publishDate	2012
url	https://ir.uitm.edu.my/id/eprint/42965/1/42965.pdf
_version_	1783734676840513536

Classification of parkinson's disease (pd) based on multilayer perceptrons (MLPs) neural network and anova as a feature extraction / Zahari Abu Bakar ... [et al.]

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