Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid

This thesis presents the investigation on the performance of Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) using Levenberg-Marquardt (LM) Algorithm in heart disease diagnosis. ANN aims to transform the inputs into the meaningful output. ANN is biological inspired and it has dynami...

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Main Author:	Ab Hamid, Salbiah
Format:	Thesis
Language:	English
Published:	2010
Subjects:	Neural networks (Computer science) Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/69513/1/69513.pdf
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id	my-uitm-ir.69513
record_format	uketd_dc
spelling	my-uitm-ir.695132023-03-27T07:58:48Z Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid 2010 Ab Hamid, Salbiah Neural networks (Computer science) Algorithms This thesis presents the investigation on the performance of Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) using Levenberg-Marquardt (LM) Algorithm in heart disease diagnosis. ANN aims to transform the inputs into the meaningful output. ANN is biological inspired and it has dynamic characteristic which is learning. ANN is able to learn through experience and adaptation. It learns the types of input based on their weights and properties. MLP consist of interconnected input layer, hidden layer and output layer. The weight of each value in hidden layers will be considered during the learning process. LM algorithm is used to minimize the error during training and testing process. A transfer function simulation model is developed by using the MATLAB software. This ANN model is developed to facilitate heart disease diagnosis. 2010 Thesis https://ir.uitm.edu.my/id/eprint/69513/ https://ir.uitm.edu.my/id/eprint/69513/1/69513.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Nairn, Nani Fadzlina
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Nairn, Nani Fadzlina
topic	Neural networks (Computer science) Algorithms
spellingShingle	Neural networks (Computer science) Algorithms Ab Hamid, Salbiah Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
description	This thesis presents the investigation on the performance of Artificial Neural Network (ANN) with Multilayer Perceptron (MLP) using Levenberg-Marquardt (LM) Algorithm in heart disease diagnosis. ANN aims to transform the inputs into the meaningful output. ANN is biological inspired and it has dynamic characteristic which is learning. ANN is able to learn through experience and adaptation. It learns the types of input based on their weights and properties. MLP consist of interconnected input layer, hidden layer and output layer. The weight of each value in hidden layers will be considered during the learning process. LM algorithm is used to minimize the error during training and testing process. A transfer function simulation model is developed by using the MATLAB software. This ANN model is developed to facilitate heart disease diagnosis.
format	Thesis
qualification_level	Bachelor degree
author	Ab Hamid, Salbiah
author_facet	Ab Hamid, Salbiah
author_sort	Ab Hamid, Salbiah
title	Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
title_short	Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
title_full	Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
title_fullStr	Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
title_full_unstemmed	Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid
title_sort	evaluation of optimal mlp structure for heart disease diagnosis / salbiah ab hamid
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2010
url	https://ir.uitm.edu.my/id/eprint/69513/1/69513.pdf
_version_	1783735889983176704

Evaluation of optimal MLP structure for heart disease diagnosis / Salbiah Ab Hamid

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