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

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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Bibliographic Details
Main Authors: Jing-xian Wang, Chang-ping Li, Zhuang Cui, Yan Liang, Yu-hang Wang, Yu Zhou, Yin Liu, Jing Gao
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
premature myocardial infarction
heart failure with preserved ejection fraction
machine learning
XGBoost
prediction
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/full
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https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/full

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