<![CDATA[Hypertension is a major comorbidity in diabetic patients, significantly increasing the risk of cardiovascular diseases and other complications. Despite advancements in machine learning for predicting hypertension, challenges such as class imbalance and lack of model interpretability remain barriers to clinical adoption. This study aims to bridge these gaps by integrating Explainable AI (XAI) techniques with resampling methods to predict hypertension risk in diabetic patients. This study applied SMOTE, SMOTE-ENN, and threshold tuning to improve model performance while maintaining interpretability. The Random Forest and XGBoost models were evaluated using accuracy, precision, recall, F1-score, and AUC. After applying SMOTE, Random Forest achieved an accuracy of 77.66%, with a recall of 33.33% and an AUC of 0.67. XGBoost with SMOTE showed an accuracy of 82.31% and recall of 23.81%, with an AUC of 0.69. The application of SMOTE-ENN improved recall to 53.33% for Random Forest, while XGBoost reached a recall of 47.62%. Threshold tuning enhanced recall to 61.90%, but decreased precision to 22.57%. The Balanced Random Forest model, after hyperparameter tuning, achieved an accuracy of 79.31% and a recall of 31.43%. This work’s key contribution is the integration of XAI techniques to enhance model transparency, improving its applicability in clinical settings. Despite improvements, further optimization of precision and recall is needed for real-world deployment, and future research should focus on validating this framework across diverse datasets.]]>
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