Predicting sports injuries using machine learning: Risk factors and early warning systems

Abstract

Sports injuries can significantly impact athletes’ performance and career longevity, making their early prediction and prevention a critical area of research. Traditional methods often fall short of capturing the complex, nonlinear interactions between various risk factors that contribute to injuries. The early prediction of sports injuries is vital for the well-being and performance optimization of athletes. This paper introduces Intrinsic Permutation Entropy Deep Learning (IPE-DL), a novel framework that synergizes permutation entropy with deep learning architectures to enhance the prediction of sports injuries. The IPE-DL method leverages the concept of permutation entropy to quantify the complexity and regularity of time-series data derived from athletes’ physiological and biomechanical signals. These entropy measures serve as critical features, capturing the inherent nonlinear dynamics within the data. The experiments demonstrate that the IPE-DL model outperforms traditional machine learning approaches and state-of-the-art deep learning models in predicting sports injuries. The proposed deep learning model is trained on a comprehensive dataset encompassing various risk factors, including athlete-specific metrics, training load parameters, and environmental conditions. Our dataset includes data from over 1,000 athletes, with a total of 100,000 training sessions recorded. The experiments demonstrate that the IPE-DL model outperforms traditional machine learning approaches and state-of-the-art deep learning models, achieving an accuracy of 92%, a sensitivity of 89%, and a specificity of 94% in predicting sports injuries. The results highlight the model’s capability to provide early warnings by identifying subtle changes in athletes’ physiological and biomechanical states that precede injuries.