A Comparative Study of Advanced Machine Learning Ensemble Techniques for Classification of Breast Cancer

Abstract

The most prevalent and the life-threatening diseases include Breast cancer worldwide. An early detection and diagnosis which are accurate is essential for improving in survival rates. In the real-time applications, models of machine learning provide powerful tools for aiding in medical professionals in diagnosing the breast cancer very accurately and efficiently. This research focuses on application of advanced ensemble techniques, such as the Random Forest, the Support Vector Machines (SVM), the K-Nearest Neighbors (KNN), the Stacking, the Boosting, and the Blending, to classify cancer of breast as either benign or malignant. After a comprehensive analysis, the Boosting emerged as the highest-performing model with an accurate precision of 0.8493 and a ROC AUC of 0.9051. These findings are align with the Sustainable Development Goal (SDG) 3, which advocates for health care and well-being, that highlighting the importance of an accessible, data-driven education in healthcare and decision of the support systems. Hence, our work suggests that these models can significantly enhance precision in diagnostic, reducing the burden on systems of healthcare, especially in underserved areas.