Machine Learning–Based prediction of complications and residual pain after total knee arthroplasty

An XGBoost-based ML model incorporating AAHKS-defined risk factors showed moderate effectiveness in predicting postoperative complications following TKA. However, the model was unable to reliably predict residual pain. These findings underscore the need for broader inclusion of joint-specific variables and imaging data in future risk adjustment frameworks to enhance personalized care in knee arthroplasty.