Version 1
: Received: 27 April 2024 / Approved: 28 April 2024 / Online: 28 April 2024 (10:33:23 CEST)
How to cite:
Mandavalli, S. Enhancing Dengue Outbreak Predictions Using Machine Learning: A Comparative Analysis of Models. Preprints2024, 2024041847. https://doi.org/10.20944/preprints202404.1847.v1
Mandavalli, S. Enhancing Dengue Outbreak Predictions Using Machine Learning: A Comparative Analysis of Models. Preprints 2024, 2024041847. https://doi.org/10.20944/preprints202404.1847.v1
Mandavalli, S. Enhancing Dengue Outbreak Predictions Using Machine Learning: A Comparative Analysis of Models. Preprints2024, 2024041847. https://doi.org/10.20944/preprints202404.1847.v1
APA Style
Mandavalli, S. (2024). Enhancing Dengue Outbreak Predictions Using Machine Learning: A Comparative Analysis of Models. Preprints. https://doi.org/10.20944/preprints202404.1847.v1
Chicago/Turabian Style
Mandavalli, S. 2024 "Enhancing Dengue Outbreak Predictions Using Machine Learning: A Comparative Analysis of Models" Preprints. https://doi.org/10.20944/preprints202404.1847.v1
Abstract
Dengue, a perilous fever-type illness transmitted by mosquitoes, remains a significant global health concern. The incidence of dengue outbreaks is primarily influenced by climate factors, contributing to fluctuating dengue cases. Therefore, this study aims to develop predictive models for dengue outbreaks employing Machine Learning (ML) techniques. Four distinct ML models, namely K-Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were employed in this study. Prior to model analysis, thorough data preprocessing was conducted. In some instances, certain attributes exhibiting sparse correlations with dengue occurrence were eliminated based on correlation analysis. Upon finalizing the models, performance evaluation was executed through comparison based on Mean Absolute Error (MAE) metrics. The findings of this comparative analysis revealed that the KNN model exhibited significantly superior performance compared to the other three models. This outcome underscores the potential of KNN in enhancing the accuracy of dengue outbreak predictions.
Keywords
dengue; gradient boosting regressor; K-nearest neighbor; Machine Learning; Random Forest; Support Vector Regressor
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
