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

Journal: Frontiers in cardiovascular medicine
Published Date: May 7, 2025

Abstract

BACKGROUND: Heart 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 patients with PMI early and quickly.

Authors

  • Jing-Xian Wang
    Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
  • Chang-Ping Li
    Department of Health Statistics, School of Public Health, Tianjin Medical University, Tianjin 300070, China.
  • Zhuang Cui
    Department of Health Statistics, College of Public Health, Tianjin Medical University, Heping District, Tianjin, P.R. China.
  • Yan Liang
    Department of Chemistry and Biochemistry, The University of Arizona, Tucson, AZ, 85721, United States.
  • Yu-Hang Wang
    Clinical School of Thoracic, Tianjin Medical University, Tianjin, China.
  • Yu Zhou
    Department of Biospectroscopy, Leibniz-Institut für Analytische Wissenschaften - ISAS - e.V., Dortmund, Germany.
  • Yin Liu
    School of Chemistry and Chemical Engineering, Shandong University, Jinan, China.
  • Jing Gao
    Department of Gastroenterology 3, Hubei University of Medicine, Renmin Hospital, Shiyan, Hubei, China.

Keywords

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