Machine Learning-Based Injury Risk Prediction Models for Athletes in Rehabilitation

Some athletes’ lack of basic knowledge of exercise mechanism, mode, method, process and intensity has led to frequent occurrence of athletic risk events such as injury, disease and even sudden death, which seriously affects the physical and mental health of athletes and even threatens their lives. In this study, the data of athletes’ injury and disease risk characteristics were collected, and the feature selection method of Least Absolute Value Convergence and Selection Operator (LASSO) combined with Boruta’s algorithm was used to preprocess the data in order to eliminate redundant features. In terms of model construction, the prediction results of support vector machine, logistic regression, random forest algorithm and deep forest algorithm were integrated by using Stacking algorithm to construct the prediction model of athletes’ injury risk. After the predictive performance of the model is examined, it is used as an intervention for injury rehabilitation to carry out comparative experiments. The results show that the fusion model can effectively extract the feature importance of injury risk factors and predict the risk probability, and the prediction effect is better than that of a single model. Meanwhile, the intervention results show that the model has excellent effects on injury rehabilitation. This study can accurately predict injuries and illnesses, prevent the occurrence of injury and illness risk events in athletes, ensure the successful realization of sports goals, and play a role in assisting injury and illness rehabilitation.