Latest revision as of 12:45, 25 August 2026

Abstract

Breast cancer is among the major causes of cancer-related mortality in women, and early diagnosis is critical to improved survival. However, traditional diagnostic methods or standalone models often lack precision and have the potential to give false-negative results. This study aims to present a hyperparameter-tuned ensemble learning model for the classification of breast benign and malignant tumors based on the Wisconsin Breast Cancer (Original) dataset. Data quality was ensured through a series of preprocessing techniques: median imputation, Zscore standardization, and the Local Outlier Factor (LOF) noise reduction algorithm. The classical Machine Learning (ML) models (SVM, Random Forest (RF), XGBoost, Bagging) were compared with the Deep Learning models (VGG16 and DenseNet121). A hybridized soft-voting ensemble of the four classical classifiers (SVM, RF, XGBoost, and Bagging) was then constructed, and its hyperparameters were tuned with the OPTUNA framework using the Treestructured Parzen Estimator (TPE), a Bayesian optimization algorithm, over 30 trials. The results of the experiments demonstrated that the Ensemble + OPTUNA model performed with an accuracy of 99.25% and a recall of 1.00 for the malignant class compared to the individual models and recent state-of-the-art methods. This is an optimized ensemble for reliable and computationally stable performance. To enhance transparency and privacy-preserving validation in clinical settings, Explainable AI (XAI) will be incorporated into future research, while Federated Learning will be enhanced to better validate the model.

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