Application of artificial intelligence for predicting injury risk in athletes: an approach using recurrent neural networks

The aim of this study is to develop and validate a model for predicting injury risk in athletes using machine learning methods to analyze time-series physiological data. The research focuses on data from runners with the high qualification, including heart rate (HR), heart rate variability (HRV), and training load, allowing for the assessment of an athlete’s physiological state over time and the identification of potential health risks.

The proposed prediction model, based on a Long Short-Term Memory (LSTM) recurrent neural network, enables the identification of periods of increased injury risk in athletes and allows for adjustments to the training process to prevent injuries. Experimental results on real-world data demonstrated a high prediction accuracy of 85%, confirming the effectivenes of the proposed approach in forecasting injury probability. Despite the model’s success, the authors recognize the need for further longitudinal studies with an expanded dataset to ensure a more precise validation of the proposed method.

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