The increasing prevalence of ischemic stroke_particularly in Banjarmasin, Indonesia_demands the development of accurate, robust, and interpretable classification models to support timely and effective clinical decision-making. Conventional approaches and standard machine learning techniques often fall short when addressing the challenges posed by highly imbalanced medical datasets (91.40% majority vs. 8.60% minority) and limited model transparency, both of which impede clinical adoption. To overcome these limitations, this study introduces a rigorously optimized framework based on the XGBoost algorithm, enhanced by the Synthetic Minority Over-sampling Technique (SMOTE) to correct for class imbalance. The methodology incorporates a structured Train-Validation-Test split, 10-fold crossvalidation, and performance assessment using mean (_) and standard deviation (_). Two hyperparameter tuning strategies were implemented, with Random Forest employed as a comparative benchmark. SHapley Addit.....

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