Assessing the Effectiveness and Design Aspects of Optimization Methods for Analyzing Big Data in Breast Cancer Prediction

Worldwide, breast cancer remains the number one cause of death for women, necessitating the timely recognition and classification of the disease to enable timely intervention. While mammography is a commonly used diagnostic tool, but its accuracy and effectiveness in identifying breast cancer are modest. In contrast, prediction using MRI (magnetic resonance imaging) has demonstrated better sensitivity. Here introduces a new strategy, the Back Propagation Boosting Recurrent Wienmed model (BPBRW), combined with a Hybrid Krill Herd African Buffalo Optimization (HKH-ABO) technique, by the use of scans in the earlier screening of breast cancer. Research works begins by training the system with MRI breast images and introducing an innovative filter for preprocessing noisy data from an MRI. The classification of images as either cancerous or benign is carried out using the suggested BPBRW using HKH-ABO methodology. Python is used for implementing the model, and its effectiveness is thoroughly evaluated. The results show, the existing study framework achieves a remarkable accuracy of 99.4%, accompanied by greatly reduced the rate of errors of 0.11% when compared to existing approaches. This breakthrough promises the effectiveness and precision of finding cancer in breasts, potentially saving countless lives through earlier diagnosis and intervention.