Sex-Specific Physical Fitness Profiles in Chilean Adolescents: An Unsupervised Machine Learning and Explainable Artificial Intelligence Approach

Background and objective: During adolescence, physical fitness attributes are a key factor positively influencing not only physical health, but also cognitive, psychological and social development. The objective was to derive data-driven physical fitness profiles and to characterize their sex-specific structure in Chilean adolescents using unsupervised machine learning and an explainable artificial intelligence (AI) framework. Methods: This cross-sectional study included a nationally representative sample of 25,912 Chilean schoolchildren (14,184 boys and 11,728 girls) aged 13 to 17 years. Six unsupervised clustering algorithms were applied separately by sex, each configured to partition participants into three clusters representing low, medium, and high physical performance levels. Results: K-means achieved the highest composite internal validity score for both sexes (boys: 0.959, Calinski–Harabasz 5757.7, Davies–Bouldin 1.203; girls: 0.976, Calinski–Harabasz 4156.1, Davies–Bouldin 1.349), and the partition was highly stable across 500 age-stratified bootstrap resamples (adjusted Rand index 0.973 in boys and 0.967 in girls). A Random-Forest surrogate reproduced the cluster labels with high fidelity (macro-averaged F1 0.984 in both sexes; Cohen’s κ 0.973 in boys and 0.975 in girls). Conclusions: Unsupervised machine learning with explainable AI provides a robust, transparent, sex-specific framework for stratifying adolescent fitness; cardiorespiratory capacity and abdominal endurance are the components that most consistently distinguish the profiles, while lower-limb power and upper-body endurance differentiate the higher performance tiers.