Machine learning applications in sports injury prediction: A narrative review

Abstract

In recent years, machine learning (ML) has been increasingly applied to sports injury prediction, offering potential support for the early identification of risk and the optimization of preventive strategies. However, current studies face several key challenges, including the absence of standardized model development procedures and inconsistencies in data preprocessing, feature selection, and model evaluation across investigations. This narrative review systematically searched the literature published up to December 2024 in major databases (Web of Science, Scopus, PubMed, and SPORTDiscus) and synthesized the methodological progress in ML-based injury prediction. Specifically, it highlights critical stages in model development, including data preprocessing, feature engineering, model selection and comparison, evaluation metrics, and approaches to interpretability. The findings indicate that, while some ML models demonstrate promising predictive accuracy, their limited interpretability constrains clinical applicability. Furthermore, substantial heterogeneity among the included studies, such as differences in populations, injury sites, and risk factors, limits meaningful comparison of methodological performance. Future research should prioritize external validation in more diverse populations and real-world contexts while advancing interpretability and generalizability, thereby strengthening the translational potential of ML-based injury prediction. This review provides a structured framework and direction for researchers aiming to improve methodological rigor and clinical utility in this emerging field.

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