Integration of Enhanced Feature Selection with Explainable Deep Learning Models for Predicting Breast Cancer

Breast cancer continues to be a major killer among females globally, underlining the importance of accurate and early diagnosis. This study introduces a more robust feature selection process with integrated explainable machine learning models to enhance the accuracy of breast cancer classification prediction. The approach involves sophisticated preprocessing processes to address the imbalance and noise in data, followed by the hybrid feature selection method that integrates combination of Explainable techniques and CNN models. Explainable AI methods incorporated help clinicians interpret the reasoning behind model predictions. Experimental results on benchmark breast cancer datasets show that the proposed method dramatically enhances classification accuracy of 95.6 %, 95.3 % recall, and 95.13 % precision, and an AUC of 0.973, outperforming existing models. The explainable results give actionable information about the most impactful biomarkers. In addition, cross-validation ensures the generalizability and stability of the models. The framework allows for accurate prediction and human-interpretable explanation, which are both crucial for clinical decision support. Real-time model deployment and incorporation with imaging data are future extensions towards a complete diagnostic system.