Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry

BACKGROUND: Machine learning (ML) has emerged as a promising tool for risk stratification. However, few studies have applied ML to risk assessment of patients with atrial fibrillation (AF). HYPOTHESIS: We aimed to compare the performance of random forest (RF), logistic regression (LR), and conventio...

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Detalles Bibliográficos
Autores principales: Watanabe, Eiichi, Noyama, Shunsuke, Kiyono, Ken, Inoue, Hiroshi, Atarashi, Hirotsugu, Okumura, Ken, Yamashita, Takeshi, Lip, Gregory Y. H., Kodani, Eitaro, Origasa, Hideki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wiley Periodicals, Inc. 2021
Materias:
Clinical Investigations
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427975/
https://www.ncbi.nlm.nih.gov/pubmed/34318510
http://dx.doi.org/10.1002/clc.23688

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8427975/
https://www.ncbi.nlm.nih.gov/pubmed/34318510
http://dx.doi.org/10.1002/clc.23688

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