An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM

Sports injury and its rehabilitation is a particular aspect of the athletic performance management model. It contributes a significant part in the satisfactory recovery of a competitor and long-term physical health. This work examines sports rehabilitation history of athletes and based on these insights forecast results with the help of synthesis of Recurrent Neural Networks (RNNs) and Big Data analytics. This will result in optimized rehabilitation protocols and accurately anticipate recovery trajectories. The study initializes by framing an inclusive methodology for data collection, preprocessing, and RNN model implementation. The dataset included athlete profiles, adjacency injuries, treatment routes, and corresponding outcomes. Secondly, it undergoes preprocessing for ensuring data consistency, completeness, and relevance. Thirdly, Recurrent Neural Networks (RNNs) are applied for demonstrating a predictive model which will capture temporal dependencies in sequential data and make it align with the dynamic nature of rehabilitation processes. The RNN architecture is composed of multiple layers of Long Short-Term Memory (LSTM) cells to capture temporal patterns and accommodate the sequential nature of rehabilitation data. The model is then passed through training and validation procedures using historical data which assists it in ascertaining the relationship between rehabilitation protocols and outcomes. To determine the performance of sketched model different performance metrices are considered. It shows an accuracy of 85.2%, precision of 78.6%, recall of 87.9%, F1-score of 82.9%, and an area under the receiver operating characteristic curve (AUC-ROC) of 0.91. These metrics shows the model's robustness and effectiveness in catching temporal dependencies within rehabilitation data and accurately estimating outcomes.