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Article Abstract
Anterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best , RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL strain and force. Female athletes showed higher rotation during cuts, elevating ACL strain and risk.
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| http://dx.doi.org/10.1080/10255842.2025.2551846 | DOI Listing |
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