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.
