Human Breast Cancer Classification Employing the Machine Learning Ensemble
Sreenivas Mekala, S. Srinivasulu Raju, M. Gomathi, K. Naga Venkateshwara Rao, D. Kothandaraman, Saurabh Sharma · 2024
The primary driver of death for women is breast cancer (BC). For the investigation of cancer, the exact order of breast cancer information is fundamental, and patients can save money by avoiding unnecessary surgeries by understanding the difference between benign and malignant tumors. Machine learning is frequently employed in the prediction of breast cancer since it has the benefit of identifying significant traits from a set of medical data. Medical professionals working in the healthcare industry can benefit from and use these decision support tools effectively. The goal of this work is to use ensemble learning to solve the problem of categorizing breast cancer-related data. Techniques for ensemble learning are applied to enhance a classifier's performance. With the help of a Bayesian network and a radial basis function, an ensemble model for decision support is being developed in this study. This study made substantial use of the well-researched open-access dataset “Wisconsin Breast Cancer Dataset (WBCD).” Oncologists would benefit from the proposed ensemble learning by being able to discriminate cancer tumors precisely and provide patients with the best care.