Abstract
Death rate of dengue fever is low, because dengue fever become severe illness only when second infection happened. However, global warming is getting severe recently, which make the infection distribution of dengue fever different. Common method of previous studies used climate factors combined with social or geographic factors to predict dengue fever. However, recent study did not use combination of these three factors into dengue fever prediction. We proposed a method that combines these three factors with data of Taiwanese dengue fever and uses the secondary area divided by the population as the granularity. Random Forest (RF) and XGBoost (XGB) are used for prediction model of weekly dengue fever infection area. Experimental results showed that the Receiver Operator Characteristic (ROC)/Area Under the Curve (AUC) of RF and XGB are both higher than 93%, and the Recall rate is higher than 80%. With the result, government can determine which area should do disinfection process to reduce the infection rate of dengue infection. Because of accurate prediction and disinfection process, the personnel cost can be reduced and it can prevent waste of medical recourse.
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Zheng, CH., Hsu, PY., Cheng, MS., Xu, N., Chen, YC. (2022). Predicting Infection Area of Dengue Fever for Next Week Through Multiple Factors. In: Fujita, H., Fournier-Viger, P., Ali, M., Wang, Y. (eds) Advances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence. IEA/AIE 2022. Lecture Notes in Computer Science(), vol 13343. Springer, Cham. https://doi.org/10.1007/978-3-031-08530-7_7
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DOI: https://doi.org/10.1007/978-3-031-08530-7_7
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