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...

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Main Author:	Ling, Amy Mei Yin
Format:	Thesis
Language:	eng eng
Published:	2011
Subjects:	QA76 Computer software
Online Access:	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
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id	my-uum-etd.2737
record_format	uketd_dc
spelling	my-uum-etd.27372016-04-27T04:33:14Z Prediction Model for H1N1 Disease 2011 Ling, Amy Mei Yin Mohamed Din, Aniza College of Arts and Sciences (CAS) College of Arts and Sciences 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 https://etd.uum.edu.my/2737/ https://etd.uum.edu.my/2737/1/Amy_Ling_Mei_Yin.pdf application/pdf eng validuser https://etd.uum.edu.my/2737/2/1.Amy_Ling_Mei_Yin.pdf application/pdf eng public masters masters Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Mohamed Din, Aniza
topic	QA76 Computer software
spellingShingle	QA76 Computer software Ling, Amy Mei Yin Prediction Model for H1N1 Disease
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
qualification_name	masters
qualification_level	Master's degree
author	Ling, Amy Mei Yin
author_facet	Ling, Amy Mei Yin
author_sort	Ling, Amy Mei Yin
title	Prediction Model for H1N1 Disease
title_short	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_sort	prediction model for h1n1 disease
granting_institution	Universiti Utara Malaysia
granting_department	College of Arts and Sciences (CAS)
publishDate	2011
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
_version_	1747827417116835840

Prediction Model for H1N1 Disease

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