Machine learning approaches for predicting heart failure readmissions.

Journal: Postgraduate medical journal
Published Date: Jul 6, 2025

Abstract

PURPOSE: This study aims to develop and evaluate machine learning (ML) models to predict the likelihood of hospital readmission within 30 days after discharge for patients with heart failure (HF). The goal is to compare the predictive accuracy of ML models with traditional methods such as those based on Cox proportional hazards and logistic regression, to improve clinical outcomes and reduce hospital costs.

Authors

  • Amaia Pikatza-Huerga
    Faculty of Engineering, University of Deusto, Av. de las Universidades, 24, Deusto, E-48007 Bilbao, Bizkaia, Spain.
  • Aitor Almeida
    DeustoTech Institute of Technology, University of Deusto, Av. Universidades 24, 48007 Bilbao, Spain.
  • Raul Quiros
    Internal Medicine Department, Hospital Costa del Sol, 29603 Marbella, Málaga  Spain.
  • Nere Larrea
    Kronikgune, Galdakao, Spain.
  • Mari Jose Legarreta
    Kronikgune, Galdakao, Spain.
  • Unai Zulaika
    Internal Medicine Department, Hospital Costa del Sol, 29603 Marbella, Málaga  Spain.
  • Rodrigo Garcia
    Faculty of Health Sciences, University of Deusto, Av. de las Universidades, 24, Deusto, E-48007 Bilbao, Bizkaia, Spain.
  • Susana Garcia
    The Research Centre for Carbon Solutions (RCCS), School of Engineering and Physical Sciences, Heriot-Watt University EH144AS Edinburgh UK.

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