Neural Networks Classification Performance for Medical Dataset

Artificial neural networks (ANN) are designed to simulate the behavior of biological neural networks for several purposes. Neural networks (NN), with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex...

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Bibliographic Details
Main Author:	Norsarini, Salim
Format:	Thesis
Language:	eng eng
Published:	2005
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf
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id	my-uum-etd.1310
record_format	uketd_dc
spelling	my-uum-etd.13102013-07-24T12:11:23Z Neural Networks Classification Performance for Medical Dataset 2005-10-30 Norsarini, Salim Faculty of Information Technology Department of Computer Science QA71-90 Instruments and machines Artificial neural networks (ANN) are designed to simulate the behavior of biological neural networks for several purposes. Neural networks (NN), with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) are classification techniques in neural networks that were used to train historical medical data. The study was based on different data set that obtained from UCI machine learning database and tested by the WEKA software machine learning tools. The comparison results of each method were based on the training performance of classifier in terms of accuracy, training time and complexity. 2005-10 Thesis https://etd.uum.edu.my/1310/ https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf application/pdf eng validuser https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf application/pdf eng public masters masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Norsarini, Salim Neural Networks Classification Performance for Medical Dataset
description	Artificial neural networks (ANN) are designed to simulate the behavior of biological neural networks for several purposes. Neural networks (NN), with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Radial Basis Function (RBF) are classification techniques in neural networks that were used to train historical medical data. The study was based on different data set that obtained from UCI machine learning database and tested by the WEKA software machine learning tools. The comparison results of each method were based on the training performance of classifier in terms of accuracy, training time and complexity.
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Norsarini, Salim
author_facet	Norsarini, Salim
author_sort	Norsarini, Salim
title	Neural Networks Classification Performance for Medical Dataset
title_short	Neural Networks Classification Performance for Medical Dataset
title_full	Neural Networks Classification Performance for Medical Dataset
title_fullStr	Neural Networks Classification Performance for Medical Dataset
title_full_unstemmed	Neural Networks Classification Performance for Medical Dataset
title_sort	neural networks classification performance for medical dataset
granting_institution	Universiti Utara Malaysia
granting_department	Faculty of Information Technology
publishDate	2005
url	https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf
_version_	1747827118728806400

Neural Networks Classification Performance for Medical Dataset

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