[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124734-en":3,"doc-seo-124734-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124734,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence - Assessment using machine learning algorithms","Machine learning models are used to quantify how cardiovascular risk factors and treatment exposure affect cardiovascular event incidence, offering advantages over traditional scoring systems and supporting personalized cardiovascular prevention. Using cohort data from 4 sources and a study population of 3746 males, three algorithms—XGBoost, Random Forest, and Naïve Bayes—are evaluated under variable sets with and without treatment exposure. Age emerges as the most influential baseline factor. With treatment exposure, adherence-related measures become more influential than any other risk factor, changing by algorithm, and Random Forest achieves the highest accuracy (F1 0.84).","PLOS ONE  \nOPEN ACCESS  \nCitation: Castel-Feced S, Malo S, Aguilar-Palacio I, Feja-Solana C, Casasnovas JA, Maldonado L, et al.(2023) Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: Assessment using machine learning algorithms. PLoS ONE 18(11): e0293759 . [https://](https://)[ ](https://)[doi.org/10.1371/journal.pone.0293759](doi.org/10.1371/journal.pone.0293759)  \n[Editor:](Editor: Chi-Shin Wu)[ Chi-Shin Wu](Editor: Chi-Shin Wu), NHRI: National Health Research Institutes, TAIWAN  \nReceived: March 8, 2023  \nAccepted: October 19, 2023  \nPublished: November 16, 2023  \nPeer Review History: PLOS recognizes the benefits of transparency in the peer review process; therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. The editorial history of this article is available here:  \n[https://doi.org/10.1371/journal.pone.0293759](https://doi.org/10.1371/journal.pone.0293759)  \n[Copyright:](Copyright:) © [2023](2023) Castel-Feced et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nData Availability Statement: Data were provided by the Aragon Health Sciences Institute (IACS), Spain, so authors do not have permission to share  \nRESEARCH ARTICLE  \nInfluence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence: Assessment using machine learning algorithms  \nSara Castel-Feced1,2,3 *, Sara Malo1,2,3, Isabel Aguilar-Palacio1,2,3, Cristina FejaSolana2,3,4, Jos´e Antonio Casasnovas5,6, Lina Maldonado2,3,7‡, Mar´ıa Jos´e RabanaqueHern´andez1,2,3‡  \n1 Microbiology, Pediatrics, Radiology, and Public Health, University of Zaragoza, Zaragoza, Spain,  \n2 Fundaci´on Instituto de Investigaci´on Sanitaria de Arag´on (IIS Arag´on), Zaragoza, Spain, 3 GRISSA Research Group, Zaragoza, Spain, 4 Directorate of Public Health, Government of Aragon, Zaragoza, Spain, 5 Hospital Universitario Miguel Servet, Instituto de Investigaci´on Sanitaria Arag´on (IIS Arag´on), CIBERCV, Zaragoza, Spain, 6 Department of Medicine, Psychiatry and Dermatology, University of Zaragoza, Zaragoza, Spain, 7 Department of Applied Economic, University of Zaragoza, Zaragoza, Spain  \n‡ LM and MJRH also contributed equally to this work and served as senior co-authors.  \n* [scastelf@unizar.es](scastelf@unizar.es)  \nAbstract  \nAssessment 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 variablesand 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 perform","cbCaidTyb3jYVFTO","https://ap.wps.com/l/cbCaidTyb3jYVFTO","pdf",1423992,1,15,"English","en",105,"# Abstract\n## Study design and population\n## Machine learning algorithms and input variables\n## Main findings on influential factors\n## Model performance and implications","[{\"question\":\"Which cardiovascular risk factors and variables are included in the prediction models?\",\"answer\":\"The models use age, physical status, hypercholesterolemia, hypertension, and diabetes mellitus. A second variable set adds treatment exposure based on adherence for diabetes, hypertension, and hypercholesterolemia.\"},{\"question\":\"What is the most influential variable for cardiovascular event incidence in the baseline models?\",\"answer\":\"Across models without considering treatment exposure, age is identified as the most influential variable for cardiovascular event incidence.\"},{\"question\":\"How does treatment exposure affect model results and variable importance?\",\"answer\":\"When treatment exposure is included, it becomes more influential than any other cardiovascular risk factor, with its influence varying depending on the model and algorithm used. Adherence to treatment is highlighted as important for cardiovascular event risk.\"}]","Influence of cardiovascular risk factors and treatment exposure on cardiovascular event incidence - Assessment using machine learning algorithms | PDF",1785894184,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"influence-of-cardiovascular-risk-factors-and-treatment-exposure-on-cardiovascular-event-incidence-assessment-using-machine-learning-algorithms","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/influence-of-cardiovascular-risk-factors-and-treatment-exposure-on-cardiovascular-event-incidence-assessment-using-machine-learning-algorithms/124734/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which cardiovascular risk factors and variables are included in the prediction models?","Question",{"text":75,"@type":76},"The models use age, physical status, hypercholesterolemia, hypertension, and diabetes mellitus. A second variable set adds treatment exposure based on adherence for diabetes, hypertension, and hypercholesterolemia.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the most influential variable for cardiovascular event incidence in the baseline models?",{"text":80,"@type":76},"Across models without considering treatment exposure, age is identified as the most influential variable for cardiovascular event incidence.",{"name":82,"@type":73,"acceptedAnswer":83},"How does treatment exposure affect model results and variable importance?",{"text":84,"@type":76},"When treatment exposure is included, it becomes more influential than any other cardiovascular risk factor, with its influence varying depending on the model and algorithm used. 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