Predicting online motor learning after stroke in lower limb task using machine learning

Online motor learning is central to effective learning and a crucial determinant of functional recovery after stroke. Despite its clinical significance, predictors of online motor learning remain understudied. Machine Learning (ML) offers a data-driven approach to identify predictors of online motor learning that would otherwise be limited using traditional statistical approaches, such as logistic regression. This study leveraged ML to determine key predictors and their relative importance in online motor learning after stroke. One hundred and seven stroke survivors completed assessments of sociodemographic, stroke characteristics, health-related, functional, cognitive, and physical capacity. Online motor learning was quantified using a goal-directed ankle task. To predict those with and without online motor learning capacity, we applied and compared XGBoost and logistic regression approaches. The XGBoost model outperformed logistic regression, achieving a precision = 0.82, recall = 0.78, F1 score = 0.80, and an area under the precision-recall curve = 0.84. The feature importance analysis identified logical memory, DGT-backward, selective attention, functional capacity index, and physical capacity as the key predictors of online motor learning. Online motor learning after stroke is not solely a motor-driven process but is predicted by cognitive and functional capacity. ML identified multidomain predictors, offering a framework for understanding individual differences in learning potential during the session, laying the foundation for precision rehabilitation.