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...
| 第一著者: | |
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| フォーマット: | 学位論文 |
| 言語: | 英語 英語 |
| 出版事項: |
2011
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| 主題: | |
| オンライン･アクセス: | https://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf https://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf https://etd.uum.edu.my/2737/ |
| Abstract | Abstract here |
| _version_ | 1855353163890032640 |
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| author | Ling, Amy Mei Yin |
| author_facet | Ling, Amy Mei Yin |
| author_sort | Ling, Amy Mei Yin |
| 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 |
| id | oai:etd.uum.edu.my:2737 |
| institution | Universiti Utara Malaysia |
| language | English English |
| publishDate | 2011 |
| record_format | EPrints |
| record_pdf | Abstract |
| spelling | oai:etd.uum.edu.my:27372016-04-27T04:33:14Z https://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 https://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf application/pdf en https://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. |
| spellingShingle | QA76 Computer software Ling, Amy Mei Yin Prediction Model for H1N1 Disease |
| thesis_level | Master |
| title | 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_short | Prediction Model for H1N1 Disease |
| title_sort | prediction model for h1n1 disease |
| topic | QA76 Computer software |
| url | https://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf https://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf https://etd.uum.edu.my/2737/ |
| work_keys_str_mv | AT lingamymeiyin predictionmodelforh1n1disease |