Developing a multi-label learning model to predict major adverse cardiovascular events in patients with unstable angina pectoris: A prospective cohort study

The RAkEL model with Random Forest as the base classifier significantly enhanced predictive accuracy for MACE in UAP patients. This approach provides a more comprehensive risk assessment, enabling clinicians to develop personalized treatment strategies and improve patient outcomes.