Construction of Clinical Predictive Models for Heart Failure Detection Using Six Different Machine Learning Algorithms: Identification of Key Clinical Prognostic Features.

Journal: International journal of general medicine
Published Date: Dec 28, 2024

Abstract

PURPOSE: Heart failure (HF) is a clinical syndrome in which structural or functional abnormalities of the heart result in impaired ventricular filling or ejection capacity. In order to improve the adaptability of models to different patient populations and data situations. This study aims to develop predictive models for HF risk using six machine learning algorithms, providing valuable insights into the early assessment and recognition of HF by clinical features.

Authors

  • Fang Zhou Qu
    Medical School, Xizang Minzu University, Xianyang, People's Republic of China.
  • Jiang Ding
    Institute of Electrical Power Systems, Graz University of Technology, Graz, Austria.
  • Xi Feng An
    The First Affiliated Hospital of Jinan University, Guangzhou, People's Republic of China.
  • Rui Peng
    Affiliated Nanhua Hospital, University of South China, Hengyang, People's Republic of China.
  • Ni He
    Department of Cardiology, Shaanxi Provincial People's Hospital, Xi'an, People's Republic of China.
  • Sheng Liu
    Medical School, Xizang Minzu University, Xianyang, People's Republic of China.
  • Xin Jiang
    Department of Cardiology, Shaanxi Provincial People's Hospital, Xi'an, People's Republic of China.

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