Multidisciplinary prediction of running-related injuries using machine learning - Takeaways - MDSpire
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Integrative Machine Learning Approaches for Predicting Running-Related Injuries

  • By

  • Han Wu

  • Katherine Brooke-Wavell

  • Michael R. Barnes

  • Zainab Awan

  • Sarabjit Mastana

  • Sam Allen

  • Richard C. Blagrove

  • February 6, 2026

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  • 1

    The study presents a machine learning dataset for predicting running-related injuries using multidisciplinary risk factors.

  • 2

    Data was collected from 142 competitive endurance runners over 12 months, resulting in 6181 weekly samples.

  • 3

    Random forest models showed the best performance in predicting injuries, with an AUC of 0.784, significantly outperforming other algorithms.

  • 4

    Logistic regression improved performance when trained with a broader range of risk factors compared to high-quality risk factors.

  • 5

    The study provides a methodological framework for future machine learning research in sports injury prediction.

Original Source(s)

Multidisciplinary prediction of running-related injuries using machine learning

  • by Han Wu, Katherine Brooke-Wavell, Michael R. Barnes, Zainab Awan, Sarabjit Mastana, Sam Allen, Richard C. Blagrove

  • February 6, 2026

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