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Neural Networks Classification Performance for Medical Dataset

Norsarini, Salim (2005) Neural Networks Classification Performance for Medical Dataset. Masters thesis, Universiti Utara Malaysia.

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Abstract

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.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Neural Networks, Classification Techniques, Multilayer Perceptron (MLP), Support Vector Machine (SVM), Radial Basis Function (RBF), Historical Medical Data
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty and School System > Faculty of Information Technology
Depositing User: Mrs Shamsiah Mohd Shariff
Date Deposited: 08 Feb 2010 02:45
Last Modified: 24 Jul 2013 12:11
URI: http://etd.uum.edu.my/id/eprint/1310

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