Hybrid Stacking Ensemble Model for Breast Cancer Classification: Performance Optimization with Existing Machine Learning Models - Khairnar, Prerana Nilesh; Anjum, Asma; P S, Dinesh; Narayanaswamy, Prabakaran; Venkatesh, R.; Rajendran, Beaulah Jeyavathana (Indonesian Journal of Electrical Engineering and Informatics (IJEEI), 2025)
Prompt and proper recognition of breast cancer Classification (BCC) is imperative in resolution of patient results. In this paper, we suggest a new hybrid Hybrid Stacking Ensemble Model (HSEM), which combines Random Forest, Support Vector Machine and XGBoost classifiers as base learners, and logistic regression as the meta-learner. The HSEM is meant to take advantage of the complement of tree-based and kernel-based algorithms by deriving robust and generalizable binary classification of breast cancer through the use of the Wisconsin Diagnostic Breast Cancer dataset. The accuracy, ROC AUC, and feature importance analysis are considered key metrics that are rigorously tested and compared with traditional standalone models in terms of performance. Findings indicate that the HSEM performs better than traditional classifiers: its accuracy is 99%, and its AUC is 1.00, which makes the method even more viable and reliable when it comes to its prediction values. Learning curves and comparisons further confirm the efficiency of the given approach to be visualized. These results emphasize the possibility of using the Hybrid Stacking Ensemble Model as an efficient instrument of use in medical diagnosis purposes, with the subsequent benefits of providing medical professionals with better diagnostic work support options. The suggested hybrid stacking ensemble not only compares, but also improves performance by combining tree-based and kernel-based learners. The comparative evaluation shows that the optimized hybrid technique always works better than current single-model and ensemble-based methods.