Exploring Machine Learning Applications in Sports Injury Prediction Analysis

Abstract

Abstract: Injuries, particularly those resulting from repetitive strain on the body, pose a significant challenge in athletics, where prevention has traditionally relied on historical data and human expertise. Existing methods for injury prevention have struggled to achieve higher precision in practice. However, technological advancements now enable artificial intelligence (AI) and machine learning (ML) to emerge as promising tools for enhancing both injury mitigation and rehabilitation strategies. This article provides a detailed overview of recent ML advances applied to sports injury prediction and prevention. A comprehensive literature review was conducted using databases such as PubMed/Medline, IEEE/IET, and Science Direct, with additional resources from Ovid Discovery and Google Scholar, including a grey literature search. Focus was placed on studies published between 2017 and 2022, examining algorithms including K-Nearest Neighbor (KNN), K-means, decision trees, random forest, gradient boosting, AdaBoost, and neural networks. A total of 42 original research papers were reviewed and their findings summarized. While the current lack of open-source, standardized datasets and the reliance on dated regression models limit strong conclusions about ML’s real-world efficacy in sports injury prediction, addressing these challenges could enable the deployment of advanced ML architectures, thereby accelerating progress in this field and supporting the development of validated clinical tools.