Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM)
In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is inc...
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my-uum-etd.23752013-07-24T12:15:43Z Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) 2010 Zuriani, Mustaffa Yusof, Yuhanis College of Arts and Sciences (CAS) College of Arts and Sciences Q Science (General) In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is increasing rapidly in Malaysia, more work need to be done in order to prevent this situation from becoming critical. This includes work on predicting future dengue outbreak. This project proposes a prediction model incorporating Least Squares Support Vector Machines(LS-SVM) in forecasting future dengue outbreak. The data sets used in the undertaken study includes data on dengue cases data and rainfall for five districts in Selangor, from 2004-2005. Results obtained indicated that LS-SVM is capable of achieving better prediction accuracy and faster learning speed compared to Neural Network Model (NNM). 2010 Thesis https://etd.uum.edu.my/2375/ https://etd.uum.edu.my/2375/1/Zuriani_Mustaffa.pdf application/pdf eng validuser http://lintas.uum.edu.my:8080/elmu/index.jsp?module=webopac-l&action=fullDisplayRetriever.jsp&szMaterialNo=0000764990 masters masters Universiti Utara Malaysia |
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Universiti Utara Malaysia |
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UUM ETD |
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eng |
advisor |
Yusof, Yuhanis |
topic |
Q Science (General) |
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Q Science (General) Zuriani, Mustaffa Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
description |
In Malaysia, dengue fever (DF) and the potentially fatal dengue hemorrhagic fever (DHF) remain to be a significant public health concern. Higher rainfall and unconcern attitude in the community were some of the factors that contribute to the increase of dengue cases. As number of dengue cases is increasing rapidly in Malaysia, more work need to be done in order to prevent this situation from becoming critical. This includes work on predicting future dengue outbreak. This project proposes a prediction model incorporating Least Squares Support Vector Machines(LS-SVM) in forecasting future dengue outbreak. The data sets used in the undertaken study includes data on dengue cases data and rainfall for five districts in Selangor, from 2004-2005. Results obtained indicated that LS-SVM is capable of achieving better prediction accuracy and faster learning speed compared to Neural Network Model (NNM). |
format |
Thesis |
qualification_name |
masters |
qualification_level |
Master's degree |
author |
Zuriani, Mustaffa |
author_facet |
Zuriani, Mustaffa |
author_sort |
Zuriani, Mustaffa |
title |
Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
title_short |
Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
title_full |
Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
title_fullStr |
Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
title_full_unstemmed |
Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM) |
title_sort |
dengue outbreak prediction using least squares support vector machines (ls-svm) |
granting_institution |
Universiti Utara Malaysia |
granting_department |
College of Arts and Sciences (CAS) |
publishDate |
2010 |
url |
https://etd.uum.edu.my/2375/1/Zuriani_Mustaffa.pdf |
_version_ |
1747827330021064704 |