Stroke remains a major global health concern, contributing signifi cantly to mortality and long-term disability. Early and
accurate prediction can improve patient outcomes; however, traditional machine learning (ML) models often lack
transparency. In this study, we develop a stroke prediction framework that combines machine learning algorithms with
Explainable Artificial Intelligence (XAI) techniques to improve both predictive performance and model interpretability.
By imple menting and comparing three ML algorithms—Random Forest, Support Vector Machine, and Logistic
Regression—alongside two XAI methods, SHAP and LIME, this study offers a pathway toward interpretable and
trustworthy AI in medical contexts. Experimental results on the held-out test set showed that Random Forest achieved the
best performance, with 92.8% accuracy, 95.3% recall, 90.7% precision, 92.9% F1-score, and 92.8% ROC AUC.
Furthermore, SHAP analysis identified age, average glucose level, and BMI as the most influential features, while LIME
provided instance-level insights into individual predictions. The findings suggest that combining machine learning with
explainability techniques can support more transparent stroke risk prediction and may assist clinical decision-making when
further validated on larger and more diverse clinical datasets.
