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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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 |
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Universiti Teknologi MARA |
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English |
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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 |
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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 |