Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction
https://doi.org/10.4081/bse.195
Published: 29 September 2021
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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.
Sperti, M., D’Ascenzo, F. ., Navarini, L., Di Benedetto, G., Afeltra, A., Giacomelli, R., & Deriu, M. A. (2021). Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction. Biomedical Science and Engineering, 2(1). https://doi.org/10.4081/bse.195
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