Cargando…
Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations
Road traffic accidents are one of the world’s most serious problems, as they result in numerous fatalities and injuries, as well as economic losses each year. Assessing the factors that contribute to the severity of road traffic injuries has proven to be insightful. The findings may contribute to a...
Autores principales: | Dong, Sheng, Khattak, Afaq, Ullah, Irfan, Zhou, Jibiao, Hussain, Arshad |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8910532/ https://www.ncbi.nlm.nih.gov/pubmed/35270617 http://dx.doi.org/10.3390/ijerph19052925 |
Ejemplares similares
-
Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations
por: Fan, You-Wei, et al.
Publicado: (2022) -
New SHapley Additive ExPlanations (SHAP) Approach to Evaluate the Raw Materials Interactions of Steel-Fiber-Reinforced Concrete
por: Anjum, Madiha, et al.
Publicado: (2022) -
Machine Learning Models Using SHapley Additive exPlanation for Fire Risk Assessment Mode and Effects Analysis of Stadiums
por: Lu, Ying, et al.
Publicado: (2023) -
Prediction of lymph node metastasis in patients with breast invasive micropapillary carcinoma based on machine learning and SHapley Additive exPlanations framework
por: Jiang, Cong, et al.
Publicado: (2022) -
Understanding Arteriosclerotic Heart Disease Patients Using Electronic Health Records: A Machine Learning and Shapley Additive exPlanations Approach
por: Miranda, Eka, et al.
Publicado: (2023)