Predicting Infection Area of Dengue Fever for Next Week Through Multiple Factors

Cong Han Zheng, Ping Yu Hsu, Ming Shien Cheng, Ni Xu, Yu Chun Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances and Trends in Artificial Intelligence. Theory and Practices in Artificial Intelligence - 35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022, Proceedings
EditorsHamido Fujita, Philippe Fournier-Viger, Moonis Ali, Yinglin Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages77-88
Number of pages12
ISBN (Print)9783031085291
DOIs
StatePublished - 2022
Event35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022 - Kitakyushu, Japan
Duration: 19 Jul 202222 Jul 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13343 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2022
Country/TerritoryJapan
CityKitakyushu
Period19/07/2222/07/22

Keywords

  • Dengue fever
  • Imbalanced data
  • Random forest
  • XGBoost

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