Research on athlete injury prediction model based on Logistic

In today's sports field, the prevention and management of athletes' injuries have become crucial. Timely and accurate prediction of athletes' injury risk can not only help the team to make a reasonable training plan, but also greatly reduce the loss caused by injury. In this study, based on injury_data datasets, a model of athlete injury prediction was constructed by using Logistic regression model, aiming to achieve the binary classification prediction of athlete injury risk. The injury_data datasets contains several key pieces of information about athletes, such as type of exercise, intensity of training, prior injury history, etc., which are critical to predicting whether an athlete is at risk of injury. We ensure that the data used for modeling is of high quality and accuracy through elaborate pre-processing steps, including missing value processing and outlier detection.In the process of constructing the Logistic regression model, Maximum Likelihood Estimation (MLE) is used to determine the optimal model parameters. In order to evaluate the performance of the model, we use the likelihood ratio test as the evaluation criterion of model fit degree. The experimental results show that our model can effectively predict the injury risk of athletes, and its prediction accuracy meets the preliminary application requirements.Through this study, we have confirmed the feasibility and effectiveness of the application of Logistic regression model in the field of athlete injury prediction, and hope that this method can provide technical reference for subsequent research in similar fields. Future work will focus on introducing more data features and exploring more advanced prediction models to further improve the accuracy of predictions and help optimize athlete health management and injury prevention strategies.