Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents
This 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 ca...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | International Journal of Adolescence and Youth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/02673843.2024.2417903 |
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| author | Rodrigo Yáñez-Sepúlveda Rodrigo Olivares Camilo Ravelo Guillermo Cortés-Roco Juan Pablo Zavala-Crichton Claudio Hinojosa-Torres Josivaldo de Souza-Lima Matías Monsalves-Álvarez Tomás Reyes-Amigo Juan Hurtado-Almonacid Jacqueline Páez-Herrera Sandra Mahecha-Matsudo Jorge Olivares-Arancibia Vicente Javier Clemente-Suárez |
| author_facet | Rodrigo Yáñez-Sepúlveda Rodrigo Olivares Camilo Ravelo Guillermo Cortés-Roco Juan Pablo Zavala-Crichton Claudio Hinojosa-Torres Josivaldo de Souza-Lima Matías Monsalves-Álvarez Tomás Reyes-Amigo Juan Hurtado-Almonacid Jacqueline Páez-Herrera Sandra Mahecha-Matsudo Jorge Olivares-Arancibia Vicente Javier Clemente-Suárez |
| author_sort | Rodrigo Yáñez-Sepúlveda |
| collection | DOAJ |
| description | This 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 adolescents. |
| format | Article |
| id | doaj-art-7805d8f52512437aa7b0a2d7ec68ec13 |
| institution | OA Journals |
| issn | 0267-3843 2164-4527 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Adolescence and Youth |
| spelling | doaj-art-7805d8f52512437aa7b0a2d7ec68ec132025-08-20T02:19:36ZengTaylor & Francis GroupInternational Journal of Adolescence and Youth0267-38432164-45272024-12-0129110.1080/02673843.2024.2417903Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescentsRodrigo Yáñez-Sepúlveda0Rodrigo Olivares1Camilo Ravelo2Guillermo Cortés-Roco3Juan Pablo Zavala-Crichton4Claudio Hinojosa-Torres5Josivaldo de Souza-Lima6Matías Monsalves-Álvarez7Tomás Reyes-Amigo8Juan Hurtado-Almonacid9Jacqueline Páez-Herrera10Sandra Mahecha-Matsudo11Jorge Olivares-Arancibia12Vicente Javier Clemente-Suárez13Faculty Education and Social Sciences, Universidad Andres Bello, Viña del Mar, ChileEscuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso, ChileEscuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso, ChileEscuela de Educación, Universidad Viña del Mar, Viña del Mar, ChileFaculty Education and Social Sciences, Universidad Andres Bello, Viña del Mar, ChileFaculty Education and Social Sciences, Universidad Andres Bello, Viña del Mar, ChileFaculty Education and Social Sciences, Universidad Andres Bello, Viña del Mar, ChileExercise and Rehabilitation Sciences Laboratory, School of Physical Therapy, Faculty of Rehabilitation Sciences, Universidad Andres Bello, Viña del Mar, ChileObservatorio de Ciencias de la Actividad Física (OCAF), Departamento de Ciencias de la Actividad Física, Universidad de Playa Ancha, Valparaíso, ChileGrupo eFidac, Escuela de Educación Física, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileGrupo eFidac, Escuela de Educación Física, Pontificia Universidad Católica de Valparaíso, Valparaíso, ChileEspecialidad en Medicina del Deporte y Actividad Física, Facultad de Medicina y Ciencias de la Salud, Universidad Mayor, Santiago, ChileGrupo AFySE, Investigación en Actividad Fìsica y Salud Escolar, Escuela de Pedagogìa en Educación Fìsica, Facultad de Educación, Universidad de Las Américas, Santiago, ChileFaculty of Sports Sciences, Universidad Europea de Madrid, Madrid, SpainThis 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 adolescents.https://www.tandfonline.com/doi/10.1080/02673843.2024.2417903Machine learningbig dataexercisehealth |
| spellingShingle | Rodrigo Yáñez-Sepúlveda Rodrigo Olivares Camilo Ravelo Guillermo Cortés-Roco Juan Pablo Zavala-Crichton Claudio Hinojosa-Torres Josivaldo de Souza-Lima Matías Monsalves-Álvarez Tomás Reyes-Amigo Juan Hurtado-Almonacid Jacqueline Páez-Herrera Sandra Mahecha-Matsudo Jorge Olivares-Arancibia Vicente Javier Clemente-Suárez Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents International Journal of Adolescence and Youth Machine learning big data exercise health |
| title | Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| title_full | Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| title_fullStr | Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| title_full_unstemmed | Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| title_short | Use of self-organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| title_sort | use of self organizing maps for the classification of cardiometabolic risk and physical fitness in adolescents |
| topic | Machine learning big data exercise health |
| url | https://www.tandfonline.com/doi/10.1080/02673843.2024.2417903 |
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