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Related Experiment Video

Updated: Feb 8, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Multidisciplinary prediction of running-related injuries using machine learning.

Han Wu¹, Katherine Brooke-Wavell², Michael R Barnes³

¹School of Sport, Exercise and Health Sciences, Loughborough University, Loughborough, UK. h.wu4@lboro.ac.uk.

NPJ Digital Medicine

|February 6, 2026

Summary

This study developed a machine learning dataset for predicting endurance running injuries using diverse risk factors. The models showed moderate improvement in injury prediction accuracy, with Random Forest performing best.

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Area of Science:

Sports Medicine
Biomechanical Engineering
Data Science

Background:

Endurance running-related injuries (RRIs) have complex, multifactorial causes.
Existing research often overlooks multidisciplinary risk factors for individualized RRI prediction.

Purpose of the Study:

To create a machine learning-ready dataset for weekly RRI prediction.
To evaluate the efficacy of machine learning models using multidisciplinary risk factors.

Main Methods:

Collected data on genetic, historical, biomechanical, physiological, and training factors from 142 competitive runners over 12 months.
Developed and tested machine learning models using both high-evidence and broader sets of risk factors.
Prospectively monitored runners for RRIs, accumulating 6181 weekly samples.

Main Results:

Machine learning models achieved an AUC of 0.784 ± 0.014, showing moderate improvement over previous RRI prediction.
Random Forest models demonstrated the highest performance (AUC = 0.781 ± 0.016).
Logistic regression performance significantly improved with a broader set of risk factors.

Conclusions:

Introduced a reproducible framework for machine learning-based sports injury prediction.
Provided a valuable dataset for future large-scale sports injury analytics.
Highlighted the importance of multidisciplinary data and model selection for accurate RRI prediction.