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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
https://ir.uitm.edu.my/id/eprint/75407/
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spelling my.uitm.ir.754072023-03-28T11:50:21Z https://ir.uitm.edu.my/id/eprint/75407/ Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan 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 NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/75407/1/75407.pdf Dengue outbreak prediction based on meteorological data using machine learning techniques in Kota Bharu / Nor Farisha Muhamad Krishnan. (2022) Masters thesis, thesis, Universiti Teknologi MARA (UiTM).
institution Universiti Teknologi Mara
building Tun Abdul Razak Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Mara
content_source UiTM Institutional Repository
url_provider http://ir.uitm.edu.my/
language English
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
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
publishDate 2022
url https://ir.uitm.edu.my/id/eprint/75407/1/75407.pdf
https://ir.uitm.edu.my/id/eprint/75407/
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score 13.211869

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