<![CDATA[Breast cancer remains one of the most prevalent causes of cancer-related mortality among women worldwide, making early and accurate detection critically important. Machine learning techniques have been widely applied for this purpose; however, many existing studies primarily focus on predictive accuracy without providing comprehensive analysis of model optimization and interpretability. This study proposes a comparative framework integrating Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost) with Bayesian Optimization to enhance hyperparameter tuning and model performance. The Breast Cancer Wisconsin Dataset, consisting of 569 samples with 30 numerical features, is used for evaluation. The proposed approach includes data preprocessing, dataset splitting, systematic hyperparameter optimization, model training, and performance evaluation. Experimental results show that the XGBoost model achieves superior performance compared to SVM, with an accuracy of 98.24% and an Area Under the Curve (AUC) of 0.994. Further analysis indicates that the model maintains a strong balance between precision and recall, with minimal misclassification. In addition, feature importance analysis reveals that attributes related to tumor size and structural irregularities contribute significantly to the prediction results, supporting the interpretability of the model in a medical context. The main contribution of this study lies in providing a more comprehensive evaluation that combines performance comparison, optimization effectiveness, and feature-level interpretation within a unified framework. The findings demonstrate that the integration of XGBoost and Bayesian Optimization offers a reliable and interpretable approach for breast cancer classification, with strong potential for implementation in machine learning–based clinical decision support systems. Keywords: breast cancer, machine learning, XGBoost, Support Vector Machine, Bayesian Optimization.]]>
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