Prediction of recurrent ischemic stroke using machine learning from real-world data

This study demonstrates that applying machine learning models on real-world clinical data is a promising tool for predicting the risk of ischemic stroke recurrence. RUSBoost emerged as the most reliable and generalisable model for clinical risk prediction, proved effective in improving prediction accuracy and identifying patients at highest risk. While SMOTE enhanced model learning during training. The findings highlight the importance of integrating AI technologies into clinical practice to support early treatment decisions and enhance preventive interventions, opening new pathways for better patient care and reducing the health burden from recurrent stroke.