Hybrid Machine Learning Approach to Zero-Inflated Data Improves Accuracy of Dengue Prediction.

ABSTRACT:

Background

Spatiotemporal 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 of dengue incidence prediction using ML models, to understand how the influence of spatiotemporal resolution differs between quantitative and qualitative predictions of dengue incidence, and to improve the accuracy of dengue incidence prediction with zero-inflated data.

Methodology

We predicted dengue incidence at six spatiotemporal resolutions and com

SUBMITTER: Francisco ME 

PROVIDER: S-EPMC11527386 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature