Back propagation neural network and non-linear regression models for dengue outbreak prediction

Malaysia has a good dengue surveillance system but there have been insufficient findings on suitable model to predict future dengue outbreak since conventional method is still being used. This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different arc...

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Main Author:	Husin, Nor Azura
Format:	Thesis
Language:	English
Published:	2008
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/9543/1/NorAzuraHusinMFSKSM2008.pdf
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id	my-utm-ep.9543
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spelling	my-utm-ep.95432018-06-29T21:50:53Z Back propagation neural network and non-linear regression models for dengue outbreak prediction 2008-11 Husin, Nor Azura QA75 Electronic computers. Computer science Malaysia has a good dengue surveillance system but there have been insufficient findings on suitable model to predict future dengue outbreak since conventional method is still being used. This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different architectures and parameters incorporating time series, location and rainfall data to define the best architecture for early prediction of dengue outbreak. The case study covered dengue and rainfall data of five districts in Selangor from year 2004 until 2005. Four architectures of NNM and NLRM were developed in this study. Architecture I involved only dengue cases data, Architecture II involved combination of dengue cases data and rainfall data, Architecture III involved proximity location dengue cases data, while Architecture IV involved the combination of all criteria. The C programming and Matlab software were used by this artificial intelligent method to develop the NNM and NLRM. The parameters studied in this research were adjusted for optimal performance. These parameters are the learning rate, momentum rate and number of neurons in the hidden layer of architectures. The performance of overall architecture was analyzed and the result shows that the Mean Square Error (MSE) for all architectures by using NNM is better compared to NLRM. Furthermore, the results also indicate that architecture IV performs significantly better than other architectures in predicting dengue outbreak using NNM compared with NLRM. It is therefore proposed as a useful approach in the problem of time series prediction of dengue outbreak. These results can help government especially for Vector Borne Disease Control (VBDC) Section of Health Ministry to develop a contingency plan to mobilize expertise, vaccines and other supplies and equipment that may be necessary in order to face dengue epidemic issues. 2008-11 Thesis http://eprints.utm.my/id/eprint/9543/ http://eprints.utm.my/id/eprint/9543/1/NorAzuraHusinMFSKSM2008.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Husin, Nor Azura Back propagation neural network and non-linear regression models for dengue outbreak prediction
description	Malaysia has a good dengue surveillance system but there have been insufficient findings on suitable model to predict future dengue outbreak since conventional method is still being used. This study aims to design a Neural Network Model (NNM) and Nonlinear Regression Model (NLRM) using different architectures and parameters incorporating time series, location and rainfall data to define the best architecture for early prediction of dengue outbreak. The case study covered dengue and rainfall data of five districts in Selangor from year 2004 until 2005. Four architectures of NNM and NLRM were developed in this study. Architecture I involved only dengue cases data, Architecture II involved combination of dengue cases data and rainfall data, Architecture III involved proximity location dengue cases data, while Architecture IV involved the combination of all criteria. The C programming and Matlab software were used by this artificial intelligent method to develop the NNM and NLRM. The parameters studied in this research were adjusted for optimal performance. These parameters are the learning rate, momentum rate and number of neurons in the hidden layer of architectures. The performance of overall architecture was analyzed and the result shows that the Mean Square Error (MSE) for all architectures by using NNM is better compared to NLRM. Furthermore, the results also indicate that architecture IV performs significantly better than other architectures in predicting dengue outbreak using NNM compared with NLRM. It is therefore proposed as a useful approach in the problem of time series prediction of dengue outbreak. These results can help government especially for Vector Borne Disease Control (VBDC) Section of Health Ministry to develop a contingency plan to mobilize expertise, vaccines and other supplies and equipment that may be necessary in order to face dengue epidemic issues.
format	Thesis
qualification_level	Master's degree
author	Husin, Nor Azura
author_facet	Husin, Nor Azura
author_sort	Husin, Nor Azura
title	Back propagation neural network and non-linear regression models for dengue outbreak prediction
title_short	Back propagation neural network and non-linear regression models for dengue outbreak prediction
title_full	Back propagation neural network and non-linear regression models for dengue outbreak prediction
title_fullStr	Back propagation neural network and non-linear regression models for dengue outbreak prediction
title_full_unstemmed	Back propagation neural network and non-linear regression models for dengue outbreak prediction
title_sort	back propagation neural network and non-linear regression models for dengue outbreak prediction
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2008
url	http://eprints.utm.my/id/eprint/9543/1/NorAzuraHusinMFSKSM2008.pdf
_version_	1747814750904909824

Back propagation neural network and non-linear regression models for dengue outbreak prediction

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