ArticleScientific reports2025
Analyzing the causes of sports injuries in college sports activities and research on the recovery strategy using an intelligent approach.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- An explainable dual-attention transformer for predicting the sociocultural impact of global sports events.Scientific reports · 2026Article
- SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach.Scientific reports · 2025Article
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2 authors.
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Abstract
Sports injuries have become a significant concern in modern athletics, particularly among college athletes. These injuries impact athletes' physical health and affect their mental well-being and academic performance. This article explores the various causes of sports injuries in college athletics, shedding light on the high risks involved. After an in-depth analysis, it is evident that factors such as intense training pressure, environmental conditions, psychological challenges, stress, and anxiety contribute significantly to the risk of injuries. This article presents a cutting-edge recovery strategy for intelligent medical treatment to address these challenges. The approach leverages a fuzzy information-based algorithm grounded in similarity measures for spherical fuzzy sets. The proposed method aims to enhance the diagnosis of injuries, design effective rehabilitation programs, and facilitate informed decisions about athletes' return to play. This approach offers valuable insights into improving athletes' health and performance by evaluating similarity measures for spherical fuzzy sets and factoring in injury risks and recovery timelines. The findings demonstrate that integrating traditional medical techniques with advanced algorithmic strategies can significantly reduce recovery time, reduce re-injury risk, and create a more supportive environment for college athletes. The developed assessment algorithm assessed four recovery plans in this study based on four crucial factors. The recovery plan scores are 0.6449, 0.4121, 0.7185, and 0.8215 respectively. The score of the fourth plan is the highest; hence, it is considered the most suitable recovery plan for the athlete.
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