What question did this study set out to answer?

The aim is to evaluate the effectiveness of machine learning models in predicting recurrent ischemic stroke risk using clinical data.

February 6, 2026Open Access

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

NANoor Haidar Kadum AlsalmanUniversiti Sains Malaysia AAAmani Al-GhraibahJordan University of Science and Technology SGSiti Maisharah Sheikh Ghadzi

Key Points

The aim is to evaluate the effectiveness of machine learning models in predicting recurrent ischemic stroke risk using clinical data.
Utilized machine learning models applied to real-world clinical data
Evaluated the RUSBoost model for prediction accuracy
Employed SMOTE to enhance model learning during training
RUSBoost was identified as the most reliable model for clinical risk prediction
The incorporation of machine learning improved prediction accuracy significantly
AI technologies highlight new pathways for preventive interventions in stroke care

Abstract

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.

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

Key Points

Abstract

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