Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction
The explainable prediction model established based on the XGBoost algorithm can accurately predict the risk of in-hospital HFpEF in PMI patients and is available at https://hfpefpmi.shinyapps.io/apppredict/. This system is expected to assist clinicians in decision-making by providing timely, prioritized, and precise interventions for PMI patients, ultimately reducing the incidence of HFpEF and improving long-term prognosis.
Authors
- Zhuang Cui (ORCID: https://orcid.org/0000-0001-6495-766X)
- Jing Gao (ORCID: https://orcid.org/0000-0002-8619-0620)
- Yan Liang (ORCID: https://orcid.org/0000-0003-4145-3463)
- Yin Liu (ORCID: https://orcid.org/0000-0002-9737-2892)
- Yuhang Wang (ORCID: https://orcid.org/0009-0008-9763-0549)
- Yu Zhou (ORCID: https://orcid.org/0000-0002-3065-4625)
- Changping Li (ORCID: https://orcid.org/0000-0003-2657-8532)
- Jing-Xian Wang
Institutions
- Tianjin University (CN)
- Tianjin Chest Hospital (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-05-07
- DOI
- https://doi.org/10.3389/fcvm.2025.1571185
- Citations
- 4
- Primary Topic
- Cardiovascular Function and Risk Factors
- Type
- article
- Field-Weighted Citation Impact
- 5.55