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...

Full description

Saved in:

Bibliographic Details
Main Author:	Zuriani, Mustaffa
Format:	Thesis
Language:	eng
Published:	2010
Subjects:	Q Science (General)
Online Access:	https://etd.uum.edu.my/2375/1/Zuriani_Mustaffa.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uum-etd.2375
record_format	uketd_dc
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

Dengue Outbreak Prediction Using Least Squares Support Vector Machines (LS-SVM)

Similar Items