Injury Risk Prediction and Prevention Algorithm for Athletes Based on Data Mining

Abstract

The injury risk prediction and prevention algorithm for athletes employs data mining techniques to analyze various factors contributing to athlete injuries, such as training load, biomechanics, previous injuries, and environmental conditions. By collecting and analyzing vast amounts of data from wearable devices, training logs, and medical records, the algorithm identifies patterns and trends associated with injury occurrence. Using machine learning models, it predicts the likelihood of an athlete sustaining an injury based on these patterns. Additionally, the algorithm provides personalized injury prevention strategies tailored to each athlete's risk profile, including adjusting training regimens, modifying technique, and recommending recovery protocols.  Injury prevention is a critical aspect of athlete safety and performance optimization in sports. This paper proposed a novel PMin-Max injury risk prediction and prevention algorithm designed to provide comprehensive assessments of injury risks for athletes. The algorithm leverages data mining techniques to compute both best-case and worst-case probabilities of injury, enabling practitioners to understand the range of potential outcomes and associated uncertainties. By computing minimum and maximum probabilities of injury for individual athletes and generating risk scores, the algorithm facilitates tailored preventive measures based on the severity of predicted injury risks. Through simulation experiments and analysis of simulated results, we demonstrate the algorithm's effectiveness in identifying athletes with elevated injury risks and highlighting situations where injury risks are relatively low.