Deep Learning for Dengue Fever Event Detection Using Online News

Abstract

Dengue fever currently has been a hyperendemic infectious disease in Indonesia. Early detection ability of the dengue fever events are essential for a timely and effective response to prevent outbreaks. This paper presents dengue fever event detection using online news. A previous study conducted an event detection task from sentences using word frequency burst to detect an ongoing event. However, news do not only report about the event (i.e., the event of dengue fever case) but also information regarding the disease. This paper focuses on detecting an event of dengue fever from online news. An assessment of different deep learning models is reported in this paper. Using k-fold cross validation, convolutional neural network (CNN) achieved the best performance (in average, test accuracy: 80.019%, precision: 78.561%, recall: 77.747%, and f1-score: 77.234%).