Supplementary information files for "Multidisciplinary prediction of running-related injuries using machine learning"

Supplementary files for article "Multidisciplinary prediction of running-related injuries using machine learning"This paper presents a machine learning (ML)-ready running-related injury (RRI) weekly prediction dataset using evidence-based multidisciplinary risk factors. Risk factors in genetic single-nucleotide polymorphisms, history, muscular strength, biomechanics, body composition, nutrition, and training were collected from competitive endurance runners (n=142), who were prospectively monitored for 12 months for RRIs, accumulating 6,181 weekly samples. ML models were fitted using (i) risk factors with high-level supporting evidence, and (ii) a broader range of risk factors to establish a performance baseline. Model performance (AUC=0.784±0.014) showed moderate improvement compared to previous RRI prediction modelling. Comparisons among different ML methods revealed nuanced insights into the interaction between data structure and model suitability. This study introduces a reproducible methodological framework for future ML sports injury prediction research and a valuable dataset for pooling in larger-scale analytics.