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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Main Author: Zuriani, Mustaffa
Format: Thesis
Language:eng
Published: 2010
Subjects:
Online Access:https://etd.uum.edu.my/2375/1/Zuriani_Mustaffa.pdf
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spelling 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
institution Universiti Utara Malaysia
collection UUM ETD
language eng
advisor Yusof, Yuhanis
topic Q Science (General)
spellingShingle 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