Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan

Dengue fever is a mosquito-borne infection that causes a high temperature, rashes, severe headache, muscle and joint discomfort, pain behind the eyes, and, in rare cases, bleeding. Rainfall, humidity, temperature, precipitation, floods, human movement, population, and the environment are only a few...

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Main Author:	Muhamad Krishnan, Nor Farisha
Format:	Thesis
Language:	English
Published:	2022
Subjects:	Cybernetics Prediction analysis
Online Access:	https://ir.uitm.edu.my/id/eprint/75407/1/75407.pdf
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id	my-uitm-ir.75407
record_format	uketd_dc
spelling	my-uitm-ir.754072023-03-28T11:50:21Z Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan 2022 Muhamad Krishnan, Nor Farisha Cybernetics Prediction analysis Dengue fever is a mosquito-borne infection that causes a high temperature, rashes, severe headache, muscle and joint discomfort, pain behind the eyes, and, in rare cases, bleeding. Rainfall, humidity, temperature, precipitation, floods, human movement, population, and the environment are only a few of the elements that induce dengue fever, including climatic and non-meteorological elements. This study used two different datasets that are dengue data and meteorological data that aims to identify the significant meteorological variables then develop a machine learning model to predict the dengue outbreak and proposed the machine learning. Dengue outbreak can be defined as 2 or more number of reported dengue cases in 7 days in certain regions. Random Forest feature selection is used to identify the significant meteorological attributes. It showed that maximum temperature, minimum temperature, average humidity and rainfall are significant for predicting dengue outbreaks. For modelling, Artificial Neural Network (ANN) and Decision Tree (DT) model were used to predict the dengue outbreak. Both models undergo parameter tuning to optimize the model. For ANN the different number of hidden nodes and decay were used to improve the model while for DT, maximum depth and complexity parameter were varying to improve the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 69.05%, Sensitivity = 98.84%, Specificity = 3.80%), performed better than DT (Accuracy = 67.46%, Sensitivity = 97.11%, Specificity = 2.53%). The government and Vector Borne Disease Control (VBDC) may have preventive measures to handle the dengue outbreak as the meteorological parameters affect the dengue outbreak. 2022 Thesis https://ir.uitm.edu.my/id/eprint/75407/ https://ir.uitm.edu.my/id/eprint/75407/1/75407.pdf text en public masters Universiti Teknologi MARA (UiTM) Faculty of Computer and Mathematical Sciences Ahmad Zukarnain, Zuriani
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ahmad Zukarnain, Zuriani
topic	Cybernetics Prediction analysis
spellingShingle	Cybernetics Prediction analysis Muhamad Krishnan, Nor Farisha Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
description	Dengue fever is a mosquito-borne infection that causes a high temperature, rashes, severe headache, muscle and joint discomfort, pain behind the eyes, and, in rare cases, bleeding. Rainfall, humidity, temperature, precipitation, floods, human movement, population, and the environment are only a few of the elements that induce dengue fever, including climatic and non-meteorological elements. This study used two different datasets that are dengue data and meteorological data that aims to identify the significant meteorological variables then develop a machine learning model to predict the dengue outbreak and proposed the machine learning. Dengue outbreak can be defined as 2 or more number of reported dengue cases in 7 days in certain regions. Random Forest feature selection is used to identify the significant meteorological attributes. It showed that maximum temperature, minimum temperature, average humidity and rainfall are significant for predicting dengue outbreaks. For modelling, Artificial Neural Network (ANN) and Decision Tree (DT) model were used to predict the dengue outbreak. Both models undergo parameter tuning to optimize the model. For ANN the different number of hidden nodes and decay were used to improve the model while for DT, maximum depth and complexity parameter were varying to improve the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 69.05%, Sensitivity = 98.84%, Specificity = 3.80%), performed better than DT (Accuracy = 67.46%, Sensitivity = 97.11%, Specificity = 2.53%). The government and Vector Borne Disease Control (VBDC) may have preventive measures to handle the dengue outbreak as the meteorological parameters affect the dengue outbreak.
format	Thesis
qualification_level	Master's degree
author	Muhamad Krishnan, Nor Farisha
author_facet	Muhamad Krishnan, Nor Farisha
author_sort	Muhamad Krishnan, Nor Farisha
title	Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
title_short	Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
title_full	Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
title_fullStr	Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
title_full_unstemmed	Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan
title_sort	dengue outbreak prediction based on meteorological data using machine learning techniques in kota bharu / nor farisha muhamad krishnan
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2022
url	https://ir.uitm.edu.my/id/eprint/75407/1/75407.pdf
_version_	1783736052894138368

Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan

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