Predicting Dengue Fever Transmission Using Machine Learning Methods
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- 2021 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), 2022, 00, pp. 21-26
- Issue Date:
- 2022-01-20
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Predicting Dengue Fever Transmission Using Machine Learning Methods.pdf | Published version | 336.8 kB |
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Dengue Fever (DF) is one of the most common vector-borne diseases that threaten humanity, with more than a third of the world's population being at risk of contracting this disease. The accurate prediction of dengue transmission will assist decision-makers and health authorities to control it in the absence of an effective vaccine and treatment. One linear (linear regression) and three nonlinear models (support vector regression, decision trees regression, and random forest regression) were developed and compared in this study to determine the model with the highest accuracy in predicting DF transmission. The prediction models were based on DF cases reported for Jeddah city, Saudi Arabia, and on the temperature and humidity, which are two features with the greatest correlation to confirmed cases. Of the tested models, the Support Vector Classification (SVC) model has the best performance, achieving 76% of prediction accuracy, while linear regression, random forest regression, and decision trees regression achieved 52%, 55% and 57% prediction accuracy, respectively.
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