Sports injuries have become an important factor affecting athletes’ competitive performance and career, and traditional prevention methods mainly rely on empirical judgment, which lacks scientificity and precision. Constructing an efficient sports injury prediction model and formulating corresponding rehabilitation strategies are of great significance to improve athletes’ health management. In this study, we constructed a sports injury prediction model using Improved Whale Optimization Algorithm Optimized Support Vector Machine (IWOA-SVM), and analyzed it based on 1000 athletes’ records in Kaggle dataset. The traditional whale optimization algorithm was improved by Circle chaos strategy initialization, inertia weight adjustment and Cauchy variation strategy, and the prediction model was built by combining with support vector machine. Correlation analysis showed that training intensity was significantly correlated with injury likelihood (p=0.007). The results of model performance evaluation showed that the IWOA-SVM model had an accuracy of 93.92%, a precision of 92.79%, a recall of 93.52%, and an AUC value of 95.45%, which were better than the traditional machine learning methods in all indicators. The feature importance analysis showed that height, weight and training intensity were the key predictors, and the influence weights were more than 0.24. Based on the prediction results, personalized rehabilitation treatment strategies including strains, abrasions, joint sprains and contusions were developed. The prediction model provided a scientific basis for the prevention of sports injuries, and the rehabilitation strategies provided a systematic guide for the athletes’ post-injury recovery.
