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TRUVACE RECORD VERSION
record: TRV-2026-0476
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-22T03:49:49.892315Z
status: published
lens: trace
sector: sports
headline: SHAP-based interpretable machine learning for injury risk prediction in university football players: a multi-dimensional data analysis approach
dek: Sports injury prediction is crucial for university football player health, yet existing research predominantly focuses on professional athletes and lacks interpretability. Using the Kaggle "University Football Injury Prediction Dataset" (800 Chinese university players), we constructed a comprehensive 18-feature evaluation system across four dimensions: basic information, training factors, physical fitness, and lifestyle habits. We systematically compared 10 machine learning algorithms. The Support Vector Machine…
gain_title: SVM-based model using 18 features across four dimensions predicted injury risk in 800 university football players with 95.6% accuracy and 99.2% ROC-AUC, with SHAP identifying stress, sleep and balance as top factors.
problem_title: Model was developed and tested on a single Kaggle dataset of 800 Chinese university players without external validation, limiting generalizability and preventing immediate clinical deployment.
trace_subject: interpretable machine learning for injury risk prediction in university football players
gain_reading: SVM-based model using 18 features across four dimensions predicted injury risk in 800 university football players with 95.6% accuracy and 99.2% ROC-AUC, with SHAP identifying stress, sleep and balance as top factors.
gain_evidence: Support Vector Machine achieved optimal performance (95.6% accuracy, 95.7% F1-score, 99.2% ROC-AUC) | This work demonstrates the feasibility of interpretable injury risk prediction for university athletes
problem_reading: Model was developed and tested on a single Kaggle dataset of 800 Chinese university players without external validation, limiting generalizability and preventing immediate clinical deployment.
problem_evidence: this study's single-dataset design and lack of external validation limit generalizability | Prospective validation is essential before clinical deployment
quick_read: Researchers built an 18-feature model across basic information, training, fitness and lifestyle dimensions for 800 Chinese university football players from a Kaggle dataset, comparing 10 algorithms and finding SVM best at 95.6% accuracy with SHAP highlighting stress, sleep and balance as key predictors.

The result suggests lifestyle and psychological factors may be more predictive than fitness alone for this student-athlete population, but because the work relies on one retrospective dataset without external testing, its real-world prevention value remains unproven pending prospective validation.
limitation: Single-dataset design without external validation limits generalizability and requires prospective validation before clinical deployment.
tag: Automated dual reading
key_points: Compared 10 machine learning algorithms on Kaggle University Football Injury Prediction Dataset of 800 Chinese university players | Built 18-feature system across basic information, training factors, physical fitness, and lifestyle habits | SHAP analysis ranked stress level 0.10, sleep duration 0.09, and balance ability 0.08 as most important, with lifestyle factors outweighing physical fitness
rundown: The study used the Kaggle University Football Injury Prediction Dataset covering 800 Chinese university players and evaluated 10 algorithms, with SVM outperforming others on accuracy, F1-score and ROC-AUC.

Interpretability analysis found psychological stress positively correlated with injury risk while adequate sleep and balance showed protective effects, and noted lifestyle factors outweighed traditional physical fitness indicators.
sources:
- peer_reviewed | Scientific Reports | https://doi.org/10.1038/s41598-025-24144-y | 2025-11-17
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