Hybrid and Ensemble Learning Approaches for Accurate Breast Cancer Detection and Classification
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Abstract
Breast cancer is a leading cause of mortality among women worldwide, underscoring the need for accurate differentiation between malignant and benign tumors to support early diagnosis and timely treatment. Malignant tumors are invasive and often require aggressive therapy, while benign tumors are non-cancerous and localized. Advances in Machine Learning (ML) and Deep Learning (DL) have significantly enhanced diagnostic performance in healthcare. This study explores hybrid and ensemble learning approaches -including Bagging, Boosting (AdaBoost, Gradient Boosting, and XGBoost), and Stacking—combined with traditional ML classifiers such as Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Random Forest, alongside DL models including Convolutional Neural Networks and Long Short-Term Memory networks for breast cancer detection. Experiments were conducted on the Wisconsin Diagnostic Breast Cancer dataset. The workflow incorporated preprocessing, feature selection, and SMOTE-based class imbalance handling applied to training folds only. Model robustness was ensured through 10-fold stratified cross-validation with GridSearchCV hyperparameter tuning. Results show that ensemble and hybrid models outperform individual classifiers, with SVM-KNN and boosting methods demonstrating particularly strong and clinically relevant performance.
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