Optimized Hybrid Ensemble Learning Basis for Accurate Breast Cancer Detection

Abstract

Sanitizing persevering outcomes and dropping death tariffs from breast cancer depend on early precise recognition. Breast Cancer Wisconsin Diagnostic Dataset study on introduces enhanced hybrid stacking collaborative framework called DL-VMSecNet efficient chest cancer cataloguing. To prediction performance, optional model incorporates several base learners into stacking architecture. To recover data quality and minimize in redundancy, feature optimization and data on pretreatment techniques are primary used. To produce final predictions meta learner is castoff train and merge numerous classifiers. Experimental data optional framework regularly reaches precision, recall, and F1-score 0.97, suggesting stable and dependable categorization, and achieves an overall correctness of 0.97. Strong discriminative ability is also confirmed by class wise assessment solution, which successfully reduces false positive and false negative. By reducing overfitting and feature dependency problems, the suggested methodology offers better generalization and stability than current methods. Conferring to results, optional hybrid stacking ensemble model may be second hand in clinical settings as a trustworthy decision support tool for primary and precise breast cancer diagnosis.