{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T02:02:40Z","timestamp":1779760960311,"version":"3.53.1"},"reference-count":35,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T00:00:00Z","timestamp":1778544000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Obesity"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Cardiovascular risk factors in adolescence, often persist into adulthood, with low cardiorespiratory fitness representing one of the strongest predictors of future cardiovascular disease. We aimed to classify cardiovascular risk associated with low cardiorespiratory fitness in adolescents using machine learning models based on anthropometric and muscular fitness variables. A cross\u2010sectional dataset of 7,852 adolescents aged 13\u201316\u2009years was analysed. Predictors included body mass index, waist circumference, waist\u2010to\u2010height ratio, standing long jump, push\u2010ups, and sit\u2010ups, while the binary outcome was cardiovascular risk derived from maximal oxygen consumption (VO\n                    <jats:sub>2<\/jats:sub>\n                    <jats:sub>max<\/jats:sub>\n                    ) cut\u2010points. Data were standardised, class imbalance was addressed using Synthetic Minority Over\u2010sampling TEchnique (SMOTE),\u00a0and eight supervised classifiers were trained with stratified five\u2010fold cross\u2010validation and grid search. Ensemble tree\u2010based methods outperformed kernel\u2010based models. Gradient Boosting achieved the best balance between predictive performance (area under the curve\u2010receiver operating curve [AUC\u2010ROC] 0.716, F1\u2010score 0.857, recall 0.760, accuracy 0.755), followed by Random Forest. SHapley Additive exPlanations (SHAP) analyses indicated that muscular fitness measures, particularly push\u2010ups, sit\u2010ups and standing long jump, contributed most to risk classification, whereas anthropometric indicators showed lower importance. These findings suggest that machine learning models built from school\u2010based muscular fitness tests and basic anthropometry can discriminate adolescents at cardiovascular risk due to low cardiorespiratory fitness, offering a feasible, low\u2010cost strategy to support early identification and targeted physical activity interventions.\n                  <\/jats:p>","DOI":"10.1111\/cob.70081","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:00:09Z","timestamp":1778583609000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Cardiovascular Risk Using Machine Learning Based on\n                    <scp>Maximal Oxygen Consumption<\/scp>\n                    , Physical Fitness, and Anthropometry"],"prefix":"10.1111","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9311-6576","authenticated-orcid":false,"given":"Rodrigo","family":"Y\u00e1\u00f1ez\u2010Sep\u00falveda","sequence":"first","affiliation":[{"name":"Faculty Education and Humanities Universidad Andres Bello  Vi\u00f1a del Mar Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rodrigo","family":"Olivares","sequence":"additional","affiliation":[{"name":"Escuela de Ingenier\u00eda Inform\u00e1tica Universidad de Valpara\u00edso  Valpara\u00edso Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo","family":"Guzm\u00e1n\u2010Mu\u00f1oz","sequence":"additional","affiliation":[{"name":"Escuela de Kinesiolog\u00eda, Facultad de Salud Universidad Santo Tom\u00e1s  Talca Chile"},{"name":"Escuela de Pedagog\u00eda en Educaci\u00f3n F\u00edsica, Facultad de Educaci\u00f3n Universidad Aut\u00f3noma de Chile  Talca Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeny","family":"Concha\u2010Cisternas","sequence":"additional","affiliation":[{"name":"Escuela de Kinesiolog\u00eda, Facultad de Salud Universidad Santo Tom\u00e1s  Talca Chile"},{"name":"Vicerrector\u00eda de Investigaci\u00f3n e Innovaci\u00f3n Universidad Arturo Prat  Iquique Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josivaldo","family":"de Souza\u2010Lima","sequence":"additional","affiliation":[{"name":"Faculty Education and Humanities Universidad Andres Bello  Vi\u00f1a del Mar Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6190-938X","authenticated-orcid":false,"given":"Exal","family":"Garcia\u2010Carrillo","sequence":"additional","affiliation":[{"name":"Department of Physical Activity Sciences, Faculty of Education Sciences Universidad Cat\u00f3lica del Maule  Talca Chile"},{"name":"School of Education, Faculty of Human Sciences Universidad Bernardo O'Higgins  Santiago Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacqueline","family":"P\u00e1ez\u2010Herrera","sequence":"additional","affiliation":[{"name":"Grupo eFidac, Escuela de Educaci\u00f3n F\u00edsica Pontificia Universidad Cat\u00f3lica de Valpara\u00edso  Valpara\u00edso Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Rojas\u2010Valverde","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n, Desarrollo e Innovaci\u00f3n en Salud y Deporte (CIDISAD), Escuela Ciencias del Movimiento Humano y Calidad e Vida (CIEMHCAVI), Universidad Nacional  Oriente Costa Rica"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guillermo","family":"Cort\u00e9s\u2010Roco","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias de la Vida Universidad Vi\u00f1a del Mar  Vi\u00f1a del Mar 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