Predicting early neurological deterioration in acute branch atheromatous disease without reperfusion therapy: a machine learning model

BackgroundAcute branch atheromatous disease (BAD) is one of the leading contributors to morbidity and disability in Asia, and early neurological deterioration (END) is common in affected patients. This study aimed to establish machine learning models to predict the risk of END in patients without reperfusion therapy.MethodsPatients with acute BAD who did not receive reperfusion therapy were retrospectively enrolled. Core predictive features were selected by LASSO regression with bootstrap stabil