https://doi.org/10.4081/bse.195
Machine learning approaches as an alternative to traditional statistical methods in cardiovascular risk prediction
Published: 29 September 2021
Abstract Views: 391
PDF: 218
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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.
Authors
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.
How to Cite
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
Copyright (c) 2021 The Authors
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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