The Efficacy of Machine Learning Models for Predicting the Prognosis of Heart Failure: A Systematic Review and Meta-Analysis.

Journal: Cardiology
Published Date: Apr 22, 2024

Abstract

INTRODUCTION: Heart failure (HF) is a major global public health concern. The application of machine learning (ML) to identify individuals at high risk and enable early intervention is a promising approach for improving HF prognosis. We aim to systematically evaluate the performance and value of ML models for predicting HF prognosis.

Authors

  • Zhaohui Xu
    School of Mechatronic Engineering, Xi'an Technological University, Xi'an 710021, Shaanxi, China.
  • Yinqin Hu
    Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Xinyi Shao
    The Grier School, Tyrone, Pennsylvania, USA.
  • Tianyun Shi
    Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Jiahui Yang
    School of Electrical and Control Engineering, Shaanxi University of Science and Technology, Xi'an 710021, China.
  • Qiqi Wan
    Department of Cardiovascular Disease, ShuGuang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
  • Yongming Liu
    Anhui Polytechnic University, Wuhu, China.

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