Evaluation of machine learning methods for prediction of heart failure mortality and readmission: meta-analysis.

Journal: BMC cardiovascular disorders
PMID: 40189534

Abstract

BACKGROUND: Heart failure (HF) impacts nearly 6 million individuals in the U.S., with a projected 46% increase by 2030, is creating significant healthcare burdens. Predictive models, particularly machine learning (ML)-based models, offer promising solutions to identify patients at greater risk of adverse outcomes, such as mortality and hospital readmission. This review aims to assess the effectiveness of ML models in predicting HF-related outcomes, with a focus on their potential to improve patient care and clinical decision-making. We aim to assess how effectively machine learning models predict mortality and readmission in heart failure patients to improve clinical outcomes.

Authors

  • Hamed Hajishah
    Student Research Committee, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran.
  • Danial Kazemi
    Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
  • Ehsan Safaee
    Student Research Committee, Faculty of Medicine, Shahed University, Tehran, Iran.
  • Mohammad Javad Amini
    Student Research Committee, School of Medicine, Alborz University of Medical Sciences, Karaj, Iran.
  • Maral Peisepar
    School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
  • Mohammad Mahdi Tanhapour
    Student Research Committee, Tehran Medical Sciences Branch, Islamic Azad University, Tehran, Iran.
  • Arian Tavasol
    Student Research Committee, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Keywords

External Resources

Popular Topics
Recent Journals