Breast Cancer Diagnosis Using Ensemble Stacking Algorithm

Breast cancer is still a major health concern worldwide, and early detection is critical to improve patient outcomes. Traditional machine learning models for breast cancer diagnosis lack sufficient predictive power. Therefore, an ensemble stacking classifier that combines multiple machine learning algorithms, including K-Nearest Neighbors, Support Vector Machines, and Random Forests, is proposed to improve prediction accuracy. The system utilizes BUSI data, which are thoroughly preprocessed with cleaning, normalization, and data splitting. A meta-model aggregates the predictions from base models, thus leading to a more accurate and reliable prediction. Experimental results show that the ensemble method outperforms individual models, thus providing enhanced performance in breast cancer detection. It illustrates the possibilities that advanced machine learning techniques offer for medical diagnostics to better the decisions in clinics and improve the treatment of patients.