Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model

Predicting the risk of heart failure after acute myocardial infarction using an interpretable machine learning model

Background Early prediction of heart failure (HF) after acute myocardial infarction (AMI) is essential for personalized treatment. We aimed to use interpretable machine learning (ML) methods to develop a risk prediction model for HF in AMI patients.MethodsWe retrospectively included patients initial...

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Bibliographic Details
Main Authors: Qingqing Lin, Wenxiang Zhao, Hailin Zhang, Wenhao Chen, Sheng Lian, Qinyun Ruan, Zhaoyang Qu, Yimin Lin, Dajun Chai, Xiaoyan Lin
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-01-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
acute myocardial infarction
heart failure
machine learning
predict
shapley additive explanations
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2025.1444323/full
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https://www.frontiersin.org/articles/10.3389/fcvm.2025.1444323/full

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