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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
R, Venkatesh
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Computer Science & IT Electrical & Electronics Engineering

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Hybrid Stacking Ensemble Model for Breast Cancer Classification: Performance Comparison with Established Machine Learning Models R, Venkatesh; Khairnar, Prerana Nilesh; Anjum, Asma; P S, Dinesh; Narayanaswamy, Prabakaran; Jeyavathana, R. Beaulah
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 13, No 4: December 2025 (ACCEPTED PAPERS)
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v13i4.7200

Abstract

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.
Co-Authors Anjum, Asma Jeyavathana, R. Beaulah Khairnar, Prerana Nilesh Narayanaswamy, Prabakaran P S, Dinesh