Cargando…
A data-driven eXtreme gradient boosting machine learning model to predict COVID-19 transmission with meteorological drivers
COVID-19 pandemic has become a global major public health concern. Examining the meteorological risk factors and accurately predicting the incidence of the COVID-19 pandemic is an extremely important challenge. Therefore, in this study, we analyzed the relationship between meteorological factors and...
Autores principales: | Rahman, Md. Siddikur, Chowdhury, Arman Hossain |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9469970/ https://www.ncbi.nlm.nih.gov/pubmed/36099253 http://dx.doi.org/10.1371/journal.pone.0273319 |
Ejemplares similares
-
A tree based eXtreme Gradient Boosting (XGBoost) machine learning model to forecast the annual rice production in Bangladesh
por: Noorunnahar, Mst, et al.
Publicado: (2023) -
Mortality predictors in patients with COVID-19 pneumonia: a machine learning approach using eXtreme Gradient Boosting model
por: Casillas, N., et al.
Publicado: (2022) -
Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study
por: Hsu, Yin-Chen, et al.
Publicado: (2020) -
A clinical diagnostic model based on an eXtreme Gradient Boosting algorithm to distinguish type 1 diabetes
por: Tang, Xiaohan, et al.
Publicado: (2021) -
Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm
por: Deng, Ying-Hao, et al.
Publicado: (2022)