Exploiting Machine Learning Techniques to Discover Physical Fitness Patterns Among Diverse Clusters of Female College Students
Year: 2025
Type: article
Study design: Observational Study
Abstract: To target improving the physical fitness for female college students and to meet the individualized needs of different subgroups. A total of 6987 female college students from a teacher training college were assessed for physical fitness. Their fitness indices were analyzed using K-means clustering and GBDT regression algorithms. Based on physical fitness profiles, students were grouped into four categories: Category I (Normal body shape, average fitness and athletic ability); Category II (Normal body shape, good fitness and athletic ability); Category III (Short, poor fitness and athletic ability); Category IV (Overall excellent fitness). Physical development and factors influencing the 800-meter run vary across groups of female college students. This study provides insights for more targeted interventions to improve specific physical fitness.
Authors: Yibing Wang, Yunxiang Pang, Siji Wang, Yiqun Pang, Xiaobing Wang
Language: English
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4411278421