Frontiers in physiology · 2026
A hybrid deep learning framework combining CTGAN and transformers for personalized injury mitigation in elite sports.
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Teng Tiemin, Zhang Lei
Аннотация
INTRODUCTION: Optimizing training in elite sports while managing injury risk requires models that can learn from heterogeneous and imbalanced athlete records without contaminating evaluation data. This study presents PTI-GANFormer (Personalized Training and Injury Risk Prediction using Generative Adversarial Network-Former), an integrated decision-support framework. Its contribution is not CTGAN or self-attention in isolation; rather, it is the leakage-controlled combination of training-only conditional augmentation, three functionally separated attention pathways, five domain-engineered indicators, and a transparent risk-to-action recommendation layer.
METHODS: After median imputation, plausibility-aware outlier handling, categorical embedding, scaling, and feature engineering, the data were stratified into training and untouched testing partitions. CTGAN was fitted only to the training partition and generated minority-class records until each training class matched the original majority count of 7,894. The hybrid classifier distinguishes No Injury, Minor Injury, and Major Injury, while the recommendation engine converts predicted risk into advisory workload and recovery options that require practitioner review.
RESULTS: Across repeated stratified evaluation runs, PTI-GANFormer achieved an accuracy of 0.960 ± 0.004 (95% CI: 0.957-0.963), precision of 0.950 ± 0.006, recall of 0.960 ± 0.005, F1-score of 0.950 ± 0.005, and macro ROC-AUC of 0.981 ± 0.004.
DISCUSSION: These results support predictive risk stratification and decision support; they do not by themselves establish injury prevention or performance enhancement.
METHODS: After median imputation, plausibility-aware outlier handling, categorical embedding, scaling, and feature engineering, the data were stratified into training and untouched testing partitions. CTGAN was fitted only to the training partition and generated minority-class records until each training class matched the original majority count of 7,894. The hybrid classifier distinguishes No Injury, Minor Injury, and Major Injury, while the recommendation engine converts predicted risk into advisory workload and recovery options that require practitioner review.
RESULTS: Across repeated stratified evaluation runs, PTI-GANFormer achieved an accuracy of 0.960 ± 0.004 (95% CI: 0.957-0.963), precision of 0.950 ± 0.006, recall of 0.960 ± 0.005, F1-score of 0.950 ± 0.005, and macro ROC-AUC of 0.981 ± 0.004.
DISCUSSION: These results support predictive risk stratification and decision support; they do not by themselves establish injury prevention or performance enhancement.