Predicting COVID-19 mortality risk in Toronto, Canada: a comparison of tree-based and regression-based machine learning methods
Abstract Background Coronavirus disease (COVID-19) presents an unprecedented threat to global health worldwide. Accurately predicting the mortality risk among the infected individuals is crucial for prioritizing medical care and mitigating the healthcare system’s burden. The present study aimed to a...
Guardado en:
Autores principales: | Cindy Feng, George Kephart, Elizabeth Juarez-Colunga |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
BMC
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/e95c431b7b8c4736b3daa26d7bd17fa1 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Mothers Matter: Using Regression Tree Algorithms to Predict Adolescents’ Sharing of Drunk References on Social Media
por: Sebastian Kurten, et al.
Publicado: (2021) -
Machine Learning and Deterministic Approach to the Reflective Ultrasound Tomography
por: Dariusz Majerek, et al.
Publicado: (2021) -
Modeling the Spatial Distribution of Soil Nitrogen Content at Smallholder Maize Farms Using Machine Learning Regression and Sentinel-2 Data
por: Zinhle Mashaba-Munghemezulu, et al.
Publicado: (2021) -
Mapping Population Distribution Based on XGBoost Using Multisource Data
por: Xin Zhao, et al.
Publicado: (2021) -
Application of gradient tree boosting regressor for the prediction of scour depth around bridge piers
por: B. M. Sreedhara, et al.
Publicado: (2021)