An Explainable Hybrid Machine Learning Framework for Enhanced Early Detection and Comprehensive Staging of Breast Cancer

Abstract

Breast cancer remains a major global health challenge, requiring accurate and interpretable diagnostic systems for reliable clinical deployment. This paper proposes an Explainable Hybrid Machine Learning (XML) framework that integrates advanced feature extraction with interpretable classification techniques for early breast cancer detection and staging. Using benchmark datasets including WDBC, BreakHis, and CBIS-DDSM, the framework applies CLAHE-based preprocessing, PCA, and SHAP-driven Recursive Feature Elimination (SHAP-RFE) to generate an optimized Hybrid Feature Vector (HFV). Experimental results demonstrate a classification accuracy of 96.84% and sensitivity of 97.12%, outperforming conventional machine learning models while reducing overfitting. The framework further supports multi-class staging aligned with AJCC TNM criteria using a dual Explainable AI subsystem combining Grad-CAM++ and SHAP for visual and mathematical interpretability. Inclusion of molecular pathways such as MAPK and PI3K-Akt improved predictive reliability by 9.2%. The proposed system offers a robust, transparent, and clinically auditable solution for personalized breast cancer diagnosis and treatment planning.