Athlete recovery prediction: a method based on machine learning for personalized strategy design

This paper introduces a machine learning framework for predicting athlete recovery post-injury and designing personalized strategies. Integrating features like age, injury severity, training intensity, nutrition quality, sport type, and previous injuries, we employ ensemble models such as XGBoost to forecast recovery probability and duration. These predictions facilitate tailored rehabilitation paths, including rest, physiotherapy, and nutrition adjustments, to enhance efficiency and reduce reinjury risks. Simulated data with realistic correlations ensures model robustness. The XGBoost model achieves 0.848 accuracy, 0.871 AUC, and 3.086 MAE days, surpassing baselines like Logistic Regression (0.794 accuracy) and SVM (0.791 accuracy). Visualizations, including violin plots, density distributions, pairplots, heatmaps, 3D scatters, and performance bars, reveal key trends such as prolonged recovery with severe injuries and negative correlations between recovery days and success. This approach advances sports medicine by providing interpretable, data-driven insights for coaches and practitioners. Future work may incorporate real-time data for adaptive strategies, potentially transforming athlete management in competitive environments.