<![CDATA[Stroke is a critical medical condition in which false negative predictions may lead to delayed treatment and increased mortality. Therefore, predictive models in the medical domain should prioritize sensitivity (recall) in addition to overall accuracy. This study analyzes the impact of the Synthetic Minority Over-sampling Technique (SMOTE) and Random Search hyperparameter optimization on five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine (SVM), Logistic Regression, and CatBoost—for stroke disease diagnosis. Two experimental scenarios were conducted, namely models trained without SMOTE and models trained with SMOTE applied only to the training data to prevent data leakage. Model performance was evaluated using accuracy, precision, recall, and F1-score, with particular emphasis on recall due to its clinical relevance. In clinical practice, low recall may lead to false negative predictions, where high-risk stroke patients are not identified by the system, potentially resulting in delayed medical intervention. Therefore, recall is emphasized as the primary performance metric in this study. Experimental results demonstrate that SMOTE consistently improves recall across all models, while Random Search further enhances performance. CatBoost achieved the best performance with an accuracy of 96.61%, recall of 97%, and F1-score of 97%. Despite its superior performance, potential overfitting risks are critically discussed. These findings indicate that the proposed approach produces a clinically relevant decision-support model for stroke risk prediction.]]>
