Machine learning-based prediction of heart failure readmission or death: implications of choosing the right model and the right metrics.

Journal: ESC heart failure
PMID: 30810291

Abstract

AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results likely due to failure to take into account the class imbalance problem commonly seen with medical data. We developed a new ML approach to predict 30 day HF readmission or death and compared the performance of this model with other commonly used prediction models.

Authors

  • Saqib Ejaz Awan
    School of Computer Science and Software Engineering, The University of Western Australia.
  • Mohammed Bennamoun
    School of Physics, Mathematics and Computing, University of Western Australia, Australia.
  • Ferdous Sohel
    Department of Information Technology, Mathematics and Statistics, College of Science, Health, Engineering and Education, Murdoch University, Murdoch, Australia.
  • Frank Mario Sanfilippo
    School of Population and Global Health.
  • Girish Dwivedi
    Department of Medicine, The University of Western Australia, 35 Stirling Highway, CRAWLEY Western Australia 6009, Australia.

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