Prediction Model for H1N1 Disease
This research has used the H1N1 disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of H1N1 disease using Multilayer Perceptron. The experiment using WEKA...
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2011
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| Online Access: | http://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf http://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf http://etd.uum.edu.my/2737/ |
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my.uum.etd.27372016-04-27T04:33:14Z http://etd.uum.edu.my/2737/ Prediction Model for H1N1 Disease Ling, Amy Mei Yin QA76 Computer software This research has used the H1N1 disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of H1N1 disease using Multilayer Perceptron. The experiment using WEKA machine learning tool produced the best parameter's values for the datasets. The General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) has been used throughout the study as a guideline. Prediction model for H1N1 disease using MLP has been generated and MLP has performs the good result where the value of accuracy for the H1N1 disease is 88.57%. 2011 Thesis NonPeerReviewed application/pdf en http://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf application/pdf en http://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf Ling, Amy Mei Yin (2011) Prediction Model for H1N1 Disease. Masters thesis, Universiti Utara Malaysia. |
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QA76 Computer software |
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QA76 Computer software Ling, Amy Mei Yin Prediction Model for H1N1 Disease |
| description |
This research has used the H1N1 disease based on the data collected from outpatient clinics (private and public sectors) across Hong Kong with influenza like illness. The objective of this project is to develop a prediction model of H1N1 disease using Multilayer Perceptron. The experiment using WEKA machine learning tool produced the best parameter's values for the datasets. The General Methodology of Design Research (GMDR) and Knowledge Discovery in Databases (KDD) has been used throughout the study as a guideline. Prediction model for H1N1 disease using MLP has been generated and MLP has performs the good result where the value of accuracy for the H1N1 disease is 88.57%. |
| format |
Thesis |
| author |
Ling, Amy Mei Yin |
| author_facet |
Ling, Amy Mei Yin |
| author_sort |
Ling, Amy Mei Yin |
| title |
Prediction Model for H1N1 Disease |
| title_short |
Prediction Model for H1N1 Disease |
| title_full |
Prediction Model for H1N1 Disease |
| title_fullStr |
Prediction Model for H1N1 Disease |
| title_full_unstemmed |
Prediction Model for H1N1 Disease |
| title_sort |
prediction model for h1n1 disease |
| publishDate |
2011 |
| url |
http://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf http://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf http://etd.uum.edu.my/2737/ |
| _version_ |
1644276778753064960 |
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13.160551 |