Research on the Prediction Model of Basketball Player Rehabilitation Efficiency Based on Machine Learning

This paper proposes a prediction model of injury rehabilitation efficiency based on machine learning for the common sports injury problems in high-intensity basketball. The study uses a data set containing athlete biomechanical data, injury information and rehabilitation results, and establishes an effective rehabilitation efficiency prediction system by comparing the performance of multiple regression algorithms. Experimental analysis shows that the ridge regression model achieves the best effect in predicting rehabilitation efficiency, with a root mean square error (RMSE) of 0.157 and a mean absolute error (MAE) of 0.136. SHAP (SHapley Additive exPlanations) analysis further reveals the important influence of biomechanical factors such as knee angle, height, weight and jump height on rehabilitation efficiency. The results of this study can provide decision support for sports medicine experts to formulate personalized rehabilitation plans, which is helpful to improve the efficiency of injury rehabilitation and reduce the risk of injury recurrence.