A Robust, Clinical-Potential Hybrid Digital Measurement Framework for Precise and Interpretable Quantification of Breast Cancer Malignancy: Fusion Stacking Ensemble with SHAP Explainability and Visual Uncertainty Reduction

Breast cancer malignancy assessment is persistently hampered by diagnostic uncertainty, inter-observer variability, and insufficient quantitative precision in conventional histopathological and imaging-based methods. This study presents a revolutionary, Clinical-Potential hybrid digital measurement framework that establishes a new benchmark in the precise and interpretable quantification of breast cancer malignancy risk. The core innovation is a powerful stacking-based fusion measurement system that integrates four complementary advanced base learners—Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and XGBoost—with a logistic regression meta-learner. This sophisticated ensemble architecture delivers exceptional measurement accuracy and metrological reliability while fundamentally addressing the black-box nature of prior models. By leveraging SHAP (SHapley Additive exPlanations) for interpretable feature contribution analysis, the system quantifies the impact of each of the nine key morphological features (e. g. , concave pointsworst, perimeterworst, radiusworst) on the predicted malignancy probability, thereby reducing diagnostic uncertainty and providing transparent, trustworthy results. Comprehensive digital visualization of feature impacts and probability distributions further supports clinical decision-making. Evaluated on the widely used Wisconsin Breast Cancer dataset, the system achieves exceptional performance (accuracy > 98%, ROC-AUC ≈ 0. 99) while offering a clear explanation of the strongest contributing biomarkers. This interpretable hybrid fusion measurement approach not only advances digitalization in oncology but also bridges the gap between high-precision machine learning and practical, reliable clinical measurement, paving the way for more confident and evidence-based malignancy quantification. While the framework demonstrates high predictive accuracy and transparency on benchmark data, it currently serves as a robust research-oriented prototype, with large-scale clinical deployment pending further external validation.