A Novel Ensemble Bagging Classification Method for Breast Cancer Classification Using Machine Learning Techniques

Breast cancer is observed as a dangerous disease type for women in the world. The clinical experts stated that early detection of cancer helps in saving lives. To detect cancer in the early stage, medical image processing is observed as an effective field. Medical Image processing with an appropriate classification mechanism improves accuracy and image resource with minimal processing time. To detect breast cancer several machine learning techniques are evolved for cancer classification. However, those machine learning techniques are subjected to increased time consumption and limitation in the accuracy of classification. This paper proposed an Ensemble Bagging Weighted Voting Classification (EBWvc) for the classification of breast cancer. Initially, to resolve to overfit in machine learning bagging is applied for collected data. The ensemble bagging classification provides effective training to machine learning for reduced computational time and improved performance characteristics. The weighted voting is adopted for the classification of cancer in the breast. The performance of proposed EBWvc is analyzed comparatively with consideration accuracy, precision, recall, and F1-Score. The comparative analysis of results exhibited that proposed EBWvc exhibits improved performance than existing classification techniques.