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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| フォーマット: | 学位論文 |
| 言語: | 英語 英語 |
| 出版事項: |
2005
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| オンライン･アクセス: | https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf |
| 要約: | 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.
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