000129422 001__ 129422
000129422 005__ 20241125101203.0
000129422 0247_ $$2doi$$a10.1371/journal.pone.0293759
000129422 0248_ $$2sideral$$a135654
000129422 037__ $$aART-2023-135654
000129422 041__ $$aeng
000129422 100__ $$0(orcid)0000-0002-5064-3763$$aCastel-Feced, Sara$$uUniversidad de Zaragoza
000129422 245__ $$aInfluence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: assessment using machine learning algorithms
000129422 260__ $$c2023
000129422 5060_ $$aAccess copy available to the general public$$fUnrestricted
000129422 5203_ $$aAssessment of the influence of cardiovascular risk factors (CVRF) on cardiovascular event (CVE) using machine learning algorithms offers some advantages over preexisting scoring systems, and better enables personalized medicine approaches to cardiovascular prevention. Using data from four different sources, we evaluated the outcomes of three machine learning algorithms for CVE prediction using different combinations of predictive variables and analysed the influence of different CVRF-related variables on CVE prediction when included in these algorithms. A cohort study based on a male cohort of workers applying populational data was conducted. The population of the study consisted of 3746 males. For descriptive analyses, mean and standard deviation were used for quantitative variables, and percentages for categorical ones. Machine learning algorithms used were XGBoost, Random Forest and Naïve Bayes (NB). They were applied to two groups of variables: i) age, physical status, Hypercholesterolemia (HC), Hypertension, and Diabetes Mellitus (DM) and ii) these variables plus treatment exposure, based on the adherence to the treatment for DM, hypertension and HC. All methods point out to the age as the most influential variable in the incidence of a CVE. When considering treatment exposure, it was more influential than any other CVRF, which changed its influence depending on the model and algorithm applied. According to the performance of the algorithms, the most accurate was Random Forest when treatment exposure was considered (F1 score 0.84), followed by XGBoost. Adherence to treatment showed to be an important variable in the risk of having a CVE. These algorithms could be applied to create models for every population, and they can be used in primary care to manage interventions personalized for every subject.
000129422 536__ $$9info:eu-repo/grantAgreement/ES/DGA-GRISSA/B09-23R$$9info:eu-repo/grantAgreement/ES/DGA-IIU/796-2019$$9info:eu-repo/grantAgreement/ES/ISCIII-FEDER-FIS/PI17-01704
000129422 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000129422 590__ $$a2.9$$b2023
000129422 592__ $$a0.839$$b2023
000129422 591__ $$aMULTIDISCIPLINARY SCIENCES$$b32 / 134 = 0.239$$c2023$$dQ1$$eT1
000129422 593__ $$aMultidisciplinary$$c2023$$dQ1
000129422 594__ $$a6.2$$b2023
000129422 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000129422 700__ $$0(orcid)0000-0002-7194-8275$$aMalo, Sara$$uUniversidad de Zaragoza
000129422 700__ $$0(orcid)0000-0001-7293-701X$$aAguilar-Palacio, Isabel$$uUniversidad de Zaragoza
000129422 700__ $$aFeja-Solana, Cristina
000129422 700__ $$0(orcid)0000-0002-9887-2629$$aCasasnovas, José Antonio$$uUniversidad de Zaragoza
000129422 700__ $$0(orcid)0000-0003-1647-3462$$aMaldonado, Lina$$uUniversidad de Zaragoza
000129422 700__ $$0(orcid)0000-0002-6671-5661$$aRabanaque-Hernández, María José$$uUniversidad de Zaragoza
000129422 7102_ $$11007$$2610$$aUniversidad de Zaragoza$$bDpto. Medicina, Psiqu. y Derm.$$cArea Medicina
000129422 7102_ $$14014$$2623$$aUniversidad de Zaragoza$$bDpto. Economía Aplicada$$cÁrea Métodos Cuant.Econ.Empres
000129422 7102_ $$11011$$2615$$aUniversidad de Zaragoza$$bDpto. Microb.Ped.Radio.Sal.Pú.$$cÁrea Medic.Prevent.Salud Públ.
000129422 773__ $$g18, 11 (2023), e0293759 [15 pp]$$pPLoS One$$tPLoS ONE$$x1932-6203
000129422 8564_ $$s1423992$$uhttps://zaguan.unizar.es/record/129422/files/texto_completo.pdf$$yVersión publicada
000129422 8564_ $$s2534974$$uhttps://zaguan.unizar.es/record/129422/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
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000129422 951__ $$a2024-11-22-12:12:35
000129422 980__ $$aARTICLE