Machine learning models for assessing risk factors affecting health care costs: 12-month exercise-based cardiac rehabilitation.

Journal: Frontiers in public health
PMID: 38864016

Abstract

INTRODUCTION: Exercise-based cardiac rehabilitation (ECR) has proven to be effective and cost-effective dominant treatment option in health care. However, the contribution of well-known risk factors for prognosis of coronary artery disease (CAD) to predict health care costs is not well recognized. Since machine learning (ML) applications are rapidly giving new opportunities to assist health care professionals' work, we used selected ML tools to assess the predictive value of defined risk factors for health care costs during 12-month ECR in patients with CAD.

Authors

  • Arto J Hautala
    Faculty of Sports and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
  • Babooshka Shavazipour
    Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
  • Bekir Afsar
    Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.
  • Mikko P Tulppo
    Research Unit of Biomedicine and Internal Medicine, Medical Research Center Oulu, Oulu University Hospital, University of Oulu, Oulu, Finland.
  • Kaisa Miettinen
    Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.

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