Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction

Authors

  • Michela Sperti PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin
  • Fabrizio D'Ascenzo Division of Cardiology, Cardiovascular and Thoracic Department, Città della Salute e della Scienza, Turin
  • Luca Navarini Unit of Allergology, Immunology, Rheumatology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome
  • Giacomo Di Benedetto 7HC srl, Rome
  • Antonella Afeltra Unit of Allergology, Immunology, Rheumatology, Department of Medicine, Università Campus Bio-Medico di Roma, Rome
  • Roberto Giacomelli Rheumatology Unit, Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, Aquila
  • Marco A. Deriu PolitoBIOMedLab, Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Turin

DOI:

https://doi.org/10.4081/bse.195

Keywords:

Machine learning, cardiovascular risk prediction, cardiology, rheumatology, decision support systems

Abstract

Machine Learning algorithms have proven promising methodologies in improving Cardiovascular (CV) risk predictors based on traditional statistics. In the present work, two case studies are reported: CV risk prediction in patients affected by Inflammatory Arthritis, with attention to Psoriatic Arthritis, and patients who experienced Acute Coronary Syndrome.

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Published

29-09-2021

Issue

Vol. 2 No. 1 (2021)

Section

Communications

License

Copyright (c) 2021 The Authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.

How to Cite

Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction. (2021). Biomedical Science and Engineering, 2(1). https://doi.org/10.4081/bse.195