Opening the black box: interpretable machine learning for predictor finding of metabolic syndrome

OBJECTIVE: The internal workings ofmachine learning algorithms are complex and considered as low-interpretation "black box" models, making it difficult for domain experts to understand and trust these complex models. The study uses metabolic syndrome (MetS) as the entry point to analyze an...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Yan, Zhang, Xiaoxu, Razbek, Jaina, Li, Deyang, Xia, Wenjun, Bao, Liangliang, Mao, Hongkai, Daken, Mayisha, Cao, Mingqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419421/
https://www.ncbi.nlm.nih.gov/pubmed/36028865
http://dx.doi.org/10.1186/s12902-022-01121-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9419421/
https://www.ncbi.nlm.nih.gov/pubmed/36028865
http://dx.doi.org/10.1186/s12902-022-01121-4

Ejemplares similares