Enhancing breast cancer prediction through stacking ensemble and deep learning integration.

ABSTRACT: Breast cancer is one of the most common types of cancer in women and is recognized as a serious global public health issue. The increasing incidence of breast cancer emphasizes the importance of early detection, which enhances the effectiveness of treatment processes. In addressing this challenge, the importance of machine learning and deep learning technologies is increasingly recognized. The aim of this study is to evaluate the integration of ensemble models and deep learning models using stacking ensemble techniques on the Breast Cancer Wisconsin (Diagnostic) dataset and to enhance breast cancer diagnosis through this methodology. To achieve this, the efficacy of ensemble methods such as Random Forest, XGBoost, LightGBM, ExtraTrees, HistGradientBoosting, AdaBoost, GradientBoosting, and

SUBMITTER: Gurcan F 

PROVIDER: S-EPMC11888877 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature