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An application of artificial neural network classifier for medical diagnosis

In recent year, various models have been proposed for medical diagnosis, which broadly can be classified into physical-based approaches and statistical-based approaches. Uncertainty and imprecision are the most important problems in medical diagnosis, other many problems in medical diagnostic...

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Main Author: Khaleel Ibraheem, Amjed
Format: Thesis
Language:English
English
English
Published: 2013
Subjects:
TA Engineering (General). Civil engineering (General)
TA168 Systems engineering
Online Access:http://eprints.uthm.edu.my/1890/1/24p%20AMJED%20KHALEEL%20IBRAHEEM.pdf
http://eprints.uthm.edu.my/1890/2/AMJED%20KHALEEL%20IBRAHEEM%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/1890/3/AMJED%20KHALEEL%20IBRAHEEM%20WATERMARK.pdf
http://eprints.uthm.edu.my/1890/
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spelling my.uthm.eprints.18902021-10-12T04:28:54Z http://eprints.uthm.edu.my/1890/ An application of artificial neural network classifier for medical diagnosis Khaleel Ibraheem, Amjed TA Engineering (General). Civil engineering (General) TA168 Systems engineering In recent year, various models have been proposed for medical diagnosis, which broadly can be classified into physical-based approaches and statistical-based approaches. Uncertainty and imprecision are the most important problems in medical diagnosis, other many problems in medical diagnostic domains need to be represented at varying degrees of diagnosis to be solved. Moreover, classification is very important in computer-aided medical diagnosis. In this respect, Artificial Neural Network (ANN) have been successfully applied and with no doubt, they provide the ability and potentials to diagnose the diseases. Therefore, this research focuses on using ANN to classify medical data. ANN model with two layers of tunable weights were used and trained using four different backpropagation algorithms while are the gradient descent(GD), gradient descent with momentum(GDM), gradient descent with adaptive learning rate(GDA) and gradient descent with momentum and adaptive learning rate(GDX). The network was used to classify three sets of medical data taken from UCI machine learning repository. The ability of all training algorithms tested and compared to each other on all datasets. Simulation results proved the ability of ANN for medical data classification with high accuracy and excellent performance and efficiency. This research provides the possibility of reduce costs and human resources. Increasing speed to find the results of medical analysis by using ANN also contributes in saving time for both physicians and patients 2013-07 Thesis NonPeerReviewed text en http://eprints.uthm.edu.my/1890/1/24p%20AMJED%20KHALEEL%20IBRAHEEM.pdf text en http://eprints.uthm.edu.my/1890/2/AMJED%20KHALEEL%20IBRAHEEM%20COPYRIGHT%20DECLARATION.pdf text en http://eprints.uthm.edu.my/1890/3/AMJED%20KHALEEL%20IBRAHEEM%20WATERMARK.pdf Khaleel Ibraheem, Amjed (2013) An application of artificial neural network classifier for medical diagnosis. Masters thesis, Universiti Tun Hussein Onn Malaysia.
institution Universiti Tun Hussein Onn Malaysia
building UTHM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tun Hussein Onn Malaysia
content_source UTHM Institutional Repository
url_provider http://eprints.uthm.edu.my/
language English
English
English
topic TA Engineering (General). Civil engineering (General)
TA168 Systems engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TA168 Systems engineering
Khaleel Ibraheem, Amjed
An application of artificial neural network classifier for medical diagnosis
description In recent year, various models have been proposed for medical diagnosis, which broadly can be classified into physical-based approaches and statistical-based approaches. Uncertainty and imprecision are the most important problems in medical diagnosis, other many problems in medical diagnostic domains need to be represented at varying degrees of diagnosis to be solved. Moreover, classification is very important in computer-aided medical diagnosis. In this respect, Artificial Neural Network (ANN) have been successfully applied and with no doubt, they provide the ability and potentials to diagnose the diseases. Therefore, this research focuses on using ANN to classify medical data. ANN model with two layers of tunable weights were used and trained using four different backpropagation algorithms while are the gradient descent(GD), gradient descent with momentum(GDM), gradient descent with adaptive learning rate(GDA) and gradient descent with momentum and adaptive learning rate(GDX). The network was used to classify three sets of medical data taken from UCI machine learning repository. The ability of all training algorithms tested and compared to each other on all datasets. Simulation results proved the ability of ANN for medical data classification with high accuracy and excellent performance and efficiency. This research provides the possibility of reduce costs and human resources. Increasing speed to find the results of medical analysis by using ANN also contributes in saving time for both physicians and patients
format Thesis
author Khaleel Ibraheem, Amjed
author_facet Khaleel Ibraheem, Amjed
author_sort Khaleel Ibraheem, Amjed
title An application of artificial neural network classifier for medical diagnosis
title_short An application of artificial neural network classifier for medical diagnosis
title_full An application of artificial neural network classifier for medical diagnosis
title_fullStr An application of artificial neural network classifier for medical diagnosis
title_full_unstemmed An application of artificial neural network classifier for medical diagnosis
title_sort application of artificial neural network classifier for medical diagnosis
publishDate 2013
url http://eprints.uthm.edu.my/1890/1/24p%20AMJED%20KHALEEL%20IBRAHEEM.pdf
http://eprints.uthm.edu.my/1890/2/AMJED%20KHALEEL%20IBRAHEEM%20COPYRIGHT%20DECLARATION.pdf
http://eprints.uthm.edu.my/1890/3/AMJED%20KHALEEL%20IBRAHEEM%20WATERMARK.pdf
http://eprints.uthm.edu.my/1890/
_version_ 1738580918283534336
score 13.211869

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