Abstract
Timely and accurate diagnosis of breast cancer remains a critical clinical challenge. In this study, we propose Stacked Artificial Neural Network (StackANN), a robust stacking ensemble framework that integrates six classical machine learning classifiers with an Artificial Neural Network (ANN) meta-learner to enhance diagnostic precision and generalization. By incorporating the Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalance and employing SHapley Additive exPlanations (SHAP) for model interpretability. StackANN was comprehensively evaluated on Wisconsin Diagnostic Breast Cancer (WDBC) datasets, Ljubljana Breast Cancer (LBC) datasets and Wisconsin Breast Cancer Dataset (WBCD), as well as the METABRIC2 dataset for multi-subtype classification. Experimental results demonstrate that StackANN consistently outperforms individual classifiers and existing hybrid models, achieving near-perfect Recall and Area Under the Curve (AUC) values while maintaining balanced overall performance. Importantly, feature attribution analysis confirmed strong alignment with clinical diagnostic criteria, emphasizing tumor malignancy, size, and morphology as key determinants. These findings highlight StackANN as a reliable, interpretable, and clinically relevant tool with significant potential for early screening, subtype classification, and personalized treatment planning in breast cancer care.