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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| Format: | Thesis |
| Language: | en en |
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2005
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| 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 https://etd.uum.edu.my/1310/ |
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| _version_ | 1833435627728666624 |
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| author | Norsarini, Salim |
| author_facet | Norsarini, Salim |
| author_sort | Norsarini, Salim |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| 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.
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| format | Thesis |
| id | my.uum.etd-1310 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2005 |
| record_format | eprints |
| spelling | my.uum.etd-13102013-07-24T12:11:23Z https://etd.uum.edu.my/1310/ Neural Networks Classification Performance for Medical Dataset Norsarini, Salim 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. 2005-10-30 Thesis NonPeerReviewed application/pdf en https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf application/pdf en https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf Norsarini, Salim (2005) Neural Networks Classification Performance for Medical Dataset. Masters thesis, Universiti Utara Malaysia. |
| spellingShingle | QA71-90 Instruments and machines Norsarini, Salim Neural Networks Classification Performance for Medical Dataset |
| title | 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_short | Neural Networks Classification Performance for Medical Dataset |
| title_sort | neural networks classification performance for medical dataset |
| topic | QA71-90 Instruments and machines |
| url | https://etd.uum.edu.my/1310/1/NORSARINI_BT._SALIM.pdf https://etd.uum.edu.my/1310/2/1.NORSARINI_BT._SALIM.pdf https://etd.uum.edu.my/1310/ |
| url_provider | http://etd.uum.edu.my/ |