Enhancing Runner Safety: A Machine Learning Framework for ‎Injury Prediction

  • Authors

    • Ashwini Sawant Department of Electronics and Telecommunication Engineering‎, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai University (MU), ‎Mumbai, Maharashtra, India
    • Sangeeta Oswal Department of Artificial Intelligence and Data Science Engineering, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai University (MU), ‎Mumbai, Maharashtra, India
    • Manisha Chattopadhyay Department of Electronics and Telecommunication Engineering‎, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai University (MU), ‎Mumbai, Maharashtra, India
    • Utsav Mutadak Department of Electronics and Telecommunication Engineering‎, Vivekanand Education Society’s Institute of Technology (VESIT), Mumbai University (MU), ‎Mumbai, Maharashtra, India
    https://doi.org/10.14419/hf5g9c06

    Received date: October 30, 2025

    Accepted date: December 9, 2025

    Published date: December 19, 2025

  • Random Forest Classifier; Machine Learning; Injury Prediction; Data Sampling.

  • Abstract

    Avoiding injuries is crucial for athletic performance. Despite the difficulty in predicting ‎injuries, new technologies and data science applications may offer valuable information. The ‎goal was to apply machine learning to predict in juries in runners based on complete training ‎data. Over a seven-year span, 74 elite middle- and long-distance runners were used to test ‎injury prediction. Two methods of analysis were used. Initially, a time series was created that ‎represents the training load for the preceding seven days, with ten features describing the ‎training for each day. These characteristics combined subjective information on training ‎effectiveness and training effort level with data from a watch (such as duration and distance). ‎The average area under the ROC-AUC curve for the day approach were produced to be ‎‎0.9978 by a prediction system based on Random Forest Classifier machine learning model. ‎The machine learning-based method predicts a significant percentage of injuries when the ‎model is built using training data recorded on a daily basis in athlete training. All things ‎considered, the results showcase the benefits of applying machine learning to forecast injuries ‎and also customize athlete training regimens‎.

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  • How to Cite

    Sawant, A., Oswal, S. ., Chattopadhyay, M. ., & Mutadak, U. . (2025). Enhancing Runner Safety: A Machine Learning Framework for ‎Injury Prediction. International Journal of Basic and Applied Sciences, 14(8), 447-452. https://doi.org/10.14419/hf5g9c06