Flow chart for data acquisition and processing.

<div><p>Background</p><p>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...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Micanaldo Ernesto Francisco (18723706) (author)
مؤلفون آخرون:	Thaddeus M. Carvajal (8796821) (author), Kozo Watanabe (544664) (author)
منشور في:	2024
الموضوعات:	Biochemistry Molecular Biology Biotechnology Ecology Cancer Infectious Diseases Plant Biology Virology Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machines extreme gradient boosting conditional inference forest artificial neural networks impending dengue outbreaks spatiotemporal resolution differs fine spatiotemporal scales predicted dengue incidence dengue incidence may dengue incidence due generalized additive models dengue incidence prediction spatiotemporal dengue prediction quantitative pattern extraction six spatiotemporal resolutions six ml algorithms novel hybrid approach frequent zero values stage quantitative prediction dengue incidence data inflated models cannot enhance prediction accuracy qualitative dengue variables dengue incidence spatiotemporal resolutions hybrid approach quantitative prediction qualitative models prediction accuracy higher resolutions environmental variables binary variables qualitative predictions combining qualitative xlink "> study aimed rare phenomena random forests predicting zero model validation inflated data data distribution control strategies alternative solution
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Flow chart for data acquisition and processing.

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