Rodrigo Yáñez-SepúlvedaOlivares, RodrigoRodrigoOlivaresCamilo RaveloGuillermo Cortés-RocoJuan Pablo Zavala-CrichtonClaudio Hinojosa-TorresJosivaldo de Souza-LimaMatías Monsalves-ÁlvarezTomás Reyes-AmigoJuan Hurtado-AlmonacidJacqueline Páez-HerreraSandra Mahecha-MatsudoJorge Olivares-ArancibiaVicente Javier Clemente-Suárez2025-12-072025-12-072024-11-0110.1080/02673843.2024.24179032-s2.0-85208780660https://cris-uv-2.scimago.es/handle/123456789/7423WOS:001345867800001This study aimed to automatically classify physical fitness and cardiometabolic risk in a Chilean adolescent using self-organizing maps. This cross-sectional study analysed a nationally representative database from the Physical Education Quality Measurement System (n = 7197). Physical fitness and cardiometabolic risk variables were derived from anthropometric indicators. Self-Organizing maps (SOM) were employed to identify participant profiles based on an unsupervised predictive model. After implementing and training the SOM, a detailed analysis of the generated maps was conducted to interpret the revealed relationships and clusters. The analysis resulted in three classification groups, categorizing the sample into low, moderate, and high-risk levels. Students with better physical fitness exhibited lower cardiometabolic risk levels and a lower body mass index. SOM, through an unsupervised model, is a reliable tool for classifying cardiometabolic risk and physical fitness in adolescentsenacceso abiertoHealthPsychology, Developmentalarticle