Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction
by Micanaldo Ernesto Francisco, Thaddeus M. Carvajal, Kozo Watanabe
BackgroundSpatiotemporal dengue forecasting using machine learning (ML) can contribute to the development of prevention and control strategies for impending dengue outbreaks. However, training data for dengue incidence may be inflated with frequent zero values because of the rarity of cases, which lowers the prediction accuracy. This study aimed to understand the influence of spatiotemporal resolutions of data on the accuracy... Читать дальше...