162251 20251017144654.0 doi 10.3389/fcvm.2025.1579947 sideral 144808 ART-2025-144808 eng Castel-Feced, Sara Universidad de Zaragoza (orcid)0000-0002-5064-3763 Prediction of cardiovascular risk using machine-learning methods. Sex-specific differences 2025 Access copy available to the general public Unrestricted Machine learning (ML) algorithms offer some advantages over traditional scoring systems to assess the influence of cardiovascular risk factors (CVRFs) on the risk of major cardiovascular event (MACE), and could be useful in clinical practice. These algorithms can also be trained using a growing body of real world data (RWD). The aim of the study was to evaluate the MACE risk applying the XGBoost and Random Forest ML algorithms to RWD, stratifying the study population by sex, comparing the outcomes of these two algorithms.MethodsThe follow-up period of the study was from 2018 to 2020. For each algorithm, 3 models were generated, including age and different combinations of three groups of variables: blood test and blood pressure measurements; CVRFs; and medication adherence.ResultsIn this study, 52,393 subjects were included, of whom 581 suffered a MACE. The incidence of MACE was 1% in women and 1.3% in men. The most prevalent CVRF was hypertension, followed by hypercholesterolaemia in both sexes. Adherence to treatment was highest for antihypertensives and lowest for antidiabetics. In all models age was the greatest relative contributor to the risk of MACE, followed by adherence to antidiabetics. Adherence to treatment proved to be an important variable in the risk of having a MACE. Moreover, similar performance was found for RF and XGBoost algorithms.ConclusionThese findings support the use of ML to assess cardiovascular risk and guide personalized prevention strategies in primary care settings. info:eu-repo/grantAgreement/ES/DGA-GRISSA/B09-23R info:eu-repo/grantAgreement/ES/DGA-IIU/796-2019 info:eu-repo/grantAgreement/ES/ISCIII/PI22-01193 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Aguilar-Palacio, Isabel Universidad de Zaragoza (orcid)0000-0001-7293-701X Malo, Sara Universidad de Zaragoza (orcid)0000-0002-7194-8275 González-García, Juan Maldonado, Lina Universidad de Zaragoza (orcid)0000-0003-1647-3462 Rabanaque-Hernández, María José Universidad de Zaragoza (orcid)0000-0002-6671-5661 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 4014 623 Universidad de Zaragoza Dpto. Economía Aplicada Área Métodos Cuant.Econ.Empres 1011 615 Universidad de Zaragoza Dpto. Microb.Ped.Radio.Sal.Pú. Área Medic.Prevent.Salud Públ. 12 (2025), 1579947 [11 pp.] Front. cardiovasc. med. Frontiers in cardiovascular medicine 2297-055X 1393081 http://zaguan.unizar.es/record/162251/files/texto_completo.pdf Versión publicada 2333505 http://zaguan.unizar.es/record/162251/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162251 articulos driver 2025-10-17-14:37:46 ARTICLE