A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

OBJECTIVE: Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms. METHODS: We used the Korea A...

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Detalles Bibliográficos
Autores principales: Sherazi, Syed Waseem Abbas, Bae, Jang-Whan, Lee, Jong Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195401/
https://www.ncbi.nlm.nih.gov/pubmed/34115750
http://dx.doi.org/10.1371/journal.pone.0249338

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8195401/
https://www.ncbi.nlm.nih.gov/pubmed/34115750
http://dx.doi.org/10.1371/journal.pone.0249338

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