Prediction of cardiovascular risk using machine-learning methods. Sex-specific differences
These findings support the use of ML to assess cardiovascular risk and guide personalized prevention strategies in primary care settings.
Authors
- Sara Malo (ORCID: https://orcid.org/0000-0002-7194-8275)
- Sara Castel-Feced (ORCID: https://orcid.org/0000-0002-5064-3763)
- Isabel Aguilar‐Palacio (ORCID: https://orcid.org/0000-0001-7293-701X)
- Lina Maldonado (ORCID: https://orcid.org/0000-0003-1647-3462)
- María José Rabanaque (ORCID: https://orcid.org/0000-0002-6671-5661)
- Juan González (ORCID: https://orcid.org/0000-0003-3109-5586)
Institutions
- Universidad de Zaragoza (ES)
- Government of Spain (ES)
- Instituto de Salud Carlos III (ES)
- Servicio Aragonés de Salud (ES)
- Red de Investigación en Actividades Preventivas y Promoción de la Salud (ES)
- Instituto de Investigación Sanitaria Aragón (ES)
Publication Details
- Journal
- Frontiers in Cardiovascular Medicine
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fcvm.2025.1579947
- Citations
- 2
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- European Commission
- Universidad de Zaragoza
- Instituto de Salud Carlos III
- European Regional Development Fund