Random forest vs. logistic regression: Predicting angiographic in-stent restenosis after second-generation drug-eluting stent implantation

As the rate of percutaneous coronary intervention increases, in-stent restenosis (ISR) has become a burden. Random forest (RF) could be superior to logistic regression (LR) for predicting ISR due to its robustness. We developed an RF model and compared its performance with the LR one for predicting...

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Detalles Bibliográficos
Autores principales: Jiang, Zhi, Tian, Longhai, Liu, Wei, Song, Bo, Xue, Chao, Li, Tianzong, Chen, Jin, Wei, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126385/
https://www.ncbi.nlm.nih.gov/pubmed/35604911
http://dx.doi.org/10.1371/journal.pone.0268757

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126385/
https://www.ncbi.nlm.nih.gov/pubmed/35604911
http://dx.doi.org/10.1371/journal.pone.0268757

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