Improving Risk Identification of Adverse Outcomes in Chronic Heart Failure Using SMOTE+ENN and Machine Learning

PURPOSE: This study sought to develop models with good identification for adverse outcomes in patients with heart failure (HF) and find strong factors that affect prognosis. PATIENTS AND METHODS: A total of 5004 qualifying cases were selected, among which 498 cases had adverse outcomes and 4506 case...

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Detalles Bibliográficos
Autores principales: Wang, Ke, Tian, Jing, Zheng, Chu, Yang, Hong, Ren, Jia, Li, Chenhao, Han, Qinghua, Zhang, Yanbo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2021
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206455/
https://www.ncbi.nlm.nih.gov/pubmed/34149290
http://dx.doi.org/10.2147/RMHP.S310295

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8206455/
https://www.ncbi.nlm.nih.gov/pubmed/34149290
http://dx.doi.org/10.2147/RMHP.S310295

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