Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction

BackgroundHeart Failure with Preserved Ejection Fraction (HFpEF) in patients with Premature Myocardial Infarction (PMI) is a crucial factor affecting long-term prognosis. This study aims to develop a model based on a machine learning algorithm that can predict the risk of in-hospital HFpEF in patien...

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Podrobná bibliografie

Hlavní autoři:	Jing-xian Wang, Chang-ping Li, Zhuang Cui, Yan Liang, Yu-hang Wang, Yu Zhou, Yin Liu, Jing Gao
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	Frontiers Media S.A. 2025-05-01
Edice:	Frontiers in Cardiovascular Medicine
Témata:	premature myocardial infarction heart failure with preserved ejection fraction machine learning XGBoost prediction
On-line přístup:	https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/full
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