Prediction of Cardiovascular Risk Using Machine Learning Based on Maximal Oxygen Consumption, Physical Fitness, and Anthropometry.

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-sectional dataset of 7,852 adolescents aged 13-16 years was analysed. Predictors included body mass index, waist circumference, waist-to-height ratio, standing long jump, push-ups, and sit-ups, while the binary outcome was cardiovascular risk derived from maximal oxygen consumption (VO2 max) cut-points. Data were standardised, class imbalance was addressed using Synthetic Minority Over-sampling TEchnique (SMOTE), and eight supervised classifiers were trained with stratified five-fold cross-validation and grid search. Ensemble tree-based methods outperformed kernel-based models. Gradient Boosting achieved the best balance between predictive performance (area under the curve-receiver operating curve [AUC-ROC] 0.716, F1-score 0.857, recall 0.760, accuracy 0.755), followed by Random Forest. SHapley Additive exPlanations (SHAP) analyses indicated that muscular fitness measures, particularly push-ups, sit-ups 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-based muscular fitness tests and basic anthropometry can discriminate adolescents at cardiovascular risk due to low cardiorespiratory fitness, offering a feasible, low-cost strategy to support early identification and targeted physical activity interventions.