Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models

Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important...

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Published in:International Journal on Informatics Visualization
Main Author: Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
Format: Article
Language:English
Published: Politeknik Negeri Padang 2022
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139442952&doi=10.30630%2fjoiv.6.2.788&partnerID=40&md5=5130e4491c3ba09b6ec07810fb5386a5
id 2-s2.0-85139442952
spelling 2-s2.0-85139442952
Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
2022
International Journal on Informatics Visualization
6
3
10.30630/joiv.6.2.788
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139442952&doi=10.30630%2fjoiv.6.2.788&partnerID=40&md5=5130e4491c3ba09b6ec07810fb5386a5
Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change. © 2022, Politeknik Negeri Padang. All rights reserved.
Politeknik Negeri Padang
25499904
English
Article
All Open Access; Gold Open Access
author Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
spellingShingle Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
author_facet Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
author_sort Krishnan N.F.M.; Zukarnain Z.A.; Ahmad A.; Jamaludin M.
title Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
title_short Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
title_full Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
title_fullStr Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
title_full_unstemmed Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
title_sort Predicting Dengue Outbreak based on Meteorological Data Using Artificial Neural Network and Decision Tree Models
publishDate 2022
container_title International Journal on Informatics Visualization
container_volume 6
container_issue 3
doi_str_mv 10.30630/joiv.6.2.788
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139442952&doi=10.30630%2fjoiv.6.2.788&partnerID=40&md5=5130e4491c3ba09b6ec07810fb5386a5
description Dengue fever is well-known as a potentially fatal disease, and the number of cases in some areas remains uncontrolled. Despite efforts to prevent the dengue outbreak from spreading further, vectors may be to blame. Identifying what weather characteristics contribute to dengue outbreaks is important to predict the dengue outbreak. This study proposes Artificial Neural Network (ANN) and Decision Tree (DT) models based on maximum temperature, minimum temperature, total rainfall, and average humidity to predict the dengue outbreak in Kota Bharu. Different numbers of hidden nodes were used in ANN to optimize the model. Both models, ANN and DT are evaluated based on accuracy, sensitivity and specificity showing that ANN (Accuracy = 68.85%, Sensitivity = 99.71%, Specificity = 1.27%), performed better than DT (Accuracy = 67.46%, Sensitivity = 98.82%, Specificity = 2.53%). This means that ANN outperforms DT when predicting a dengue outbreak in Kota Bharu. Based on the ANN model, it can be concluded that the number of hidden nodes affects the model's accuracy. Selecting the ideal number of hidden nodes for modeling the ANN model is appropriate. Even though ANN accuracy for prediction models is greater than DT, it is still low. It can be inferred that selecting a prediction model appropriate for a variety of dataset types and levels of complexity is important. Based on these models, the government may take pre-emptive actions to enhance public awareness about climate change. © 2022, Politeknik Negeri Padang. All rights reserved.
publisher Politeknik Negeri Padang
issn 25499904
language English
format Article
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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