Communications
29 September 2021
Vol. 2 No. 1 (2021)
https://doi.org/10.4081/bse.195

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

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

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

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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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
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