Font Size:
MACHINE LEARNING TECHNIQUES TO PREDICT AND MANAGE KNEE INJURY IN SPORTS MEDICINE
Last modified: 2024-08-22
Abstract
The aim of this study is to conduct a complete review of the current state of Machine Learning (ML) in injury prediction and prevention. In recent years, there has been a growing importance in the application of ML techniques to find out and reduce risks associated with injuries, particularly in high-risk industries such as sports, healthcare, and manufacturing. The essential part of our body is knee, for sports persons commonly injuries during play games. Sports injuries result in stress & strain connected with athletic events. Sports wounds can affect soft tissue (ligaments, muscles, cartilage and tendons). Injuries are common in sports and can have significant physical, psychological and financial consequences. The aim of our study was therefore to perform a systematic review of Machine learning (ML) techniques could be used to improve injury prediction and prevention in sports. ML algorithms, play crucial role for extract accurate information from given images and it also handle the complex pattern of MRI knee related clarifications. In this paper, discuss a real-life imagery rule, ML design used for recognizes meniscus tears, bone marrow edema, and general abnormalities on knee MRI tests are accessible. Final evaluation exposes the maximum accuracy of 70.11% for support vector machine, 69.75% for KNN and 70.1% for RF Tree maximum.
Conference registration is required in order to view papers.