<![CDATA[Breast cancer remains one of the leading causes of cancer-related mortality among women, making early detection and accurate diagnosis essential. Although machine learning and deep learning approaches have achieved high classification performance, their interpretability and evaluation robustness remain limited. This study proposes a Fine-Tuned Artificial Neural Network (ANN) integrated with Explainable Artificial Intelligence (XAI) for breast cancer classification using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. Model evaluation was conducted using Stratified 5-Fold Cross-Validation, while optimization was performed through learning rate adjustment and early stopping. Model interpretability was analyzed using SHapley Additive exPlanations (SHAP), including summary, decision, force, and waterfall plots. Experimental results show that the baseline ANN achieved an average accuracy of 97.54%, precision of 97.82%, recall of 98.32%, and F1-score of 98.04%. The Fine-Tuned ANN improved performance, achieving 97.89% accuracy, 98.08% precision, 98.60% recall, and 98.32% F1-score, with better stability across folds. SHAP analysis identified worst area, worst radius, worst texture, and mean concave points as the most influential features. Furthermore, the ablation study indicated that the Sigmoid activation function, Adam optimizer, and learning rate of 0.01 produced the optimal configuration.]]>
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