A Framework Using IoT-Enabled Machine Learning Techniques for Predicting Major Injuries and Medical Sports Rehabilitation

Biomedical informatics has significantly advanced artificial intelligence (AI), allowing it to summon strength. Machine-learning techniques can support medical decision-making at both the clinical and diagnostic levels. One of the most important factors in causing harm and obstructing movement is the diagnosis of sports injuries. Despite much research, the existing method for predicting sports injuries has limitations. The possibility of harm to athletes must be considered while developing approaches for workplace accident prevention and risk reduction. To determine the causes of injuries in various settings, researchers are looking at data from a wide range of sources. So, this article describes how to identify sports-related ailments using the Internet of Things (IoT) and a novel machine learning-assisted system (NMLaS). The body area network comprises IoT sensors, which collect crucial information for diagnosing sports injuries. The brain injury monitoring framework is considered in this study. It employs a neural network tuned for this task to improve the healthcare system for sports-related brain injuries. Based on the experimental findings, the suggested NMLaS approach is quite an accuracy ratio of 87%, a precision of 90%, a recall ratio of 86%, and an F1-score of 89%, the performance ratio of 35% is assessed and contrasted with existing models.