Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2023)

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Prediction of Outbreak Periods of Dengue in Baguio City, Philippines using Machine Learning Classification Models

Authors
Jozelle C. Addawe1, *, Richelle Ann B. Juayong1, Jaime D. L. Caro1
1Department of Computer Science, University of the Philippines Diliman, Diliman, Quezon City, Philippines
*Corresponding author. Email: jcaddawe@up.edu.ph
Corresponding Author
Jozelle C. Addawe
Available Online 29 February 2024.
DOI
10.2991/978-94-6463-388-7_23How to use a DOI?
Keywords
dengue; outbreak; machine learning; classification
Abstract

Detection of possible disease outbreak is a vital role of disease surveillance. Previous studies on dengue in Baguio City, Philippines include exploratory and spatiotemporal analysis, modeling and forecasting methods, but lacks approaches for detection of outbreak. This study aims to obtain a model that may be used to predict outbreaks using variables that have been shown in literature to affect the increase of dengue cases. Machine learning classifiers such as the random forest, decision trees and gradient boosting methods are tested for their performance in classifying outbreak and non-outbreak periods in five barangays of Baguio City in 2019 to 2020. Results have shown that the random forest classifier outperforms the other two classifiers in terms of prediction accuracy, with at least 75% accuracy for predicting outbreak months. The model is further improved with average cases, relative humidity, temperature and lagged values of dengue as input variables to the random forest classifier.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
29 February 2024
ISBN
10.2991/978-94-6463-388-7_23
ISSN
2589-4900
DOI
10.2991/978-94-6463-388-7_23How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Jozelle C. Addawe
AU  - Richelle Ann B. Juayong
AU  - Jaime D. L. Caro
PY  - 2024
DA  - 2024/02/29
TI  - Prediction of Outbreak Periods of Dengue in Baguio City, Philippines using Machine Learning Classification Models
BT  - Proceedings of the Workshop on Computation: Theory and Practice (WCTP 2023)
PB  - Atlantis Press
SP  - 380
EP  - 394
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-388-7_23
DO  - 10.2991/978-94-6463-388-7_23
ID  - Addawe2024
ER  -