Deep-Random-Forest Stacking Ensemble Boosts Breast-Cancer Classification on Wisconsin Data

Breast cancer is a health challenge for women around the globe, which is one of the most frequently diagnosed cancers and a leading cause of cancer-related mortality. Early detection of breast cancer is necessary to reduce mortality and improve care. With the advancement of artificial intelligence, researchers are exploring machine learning techniques that can create models for early breast cancer detection, thereby significantly improving clinical diagnostic accuracy. In this study, we propose a hybrid machine learning model, the Deep-RandomForest Stacking Ensemble Boosts, utilizing the Wisconsin Breast Cancer Dataset. By integrating the precision of deep learning with the robustness of random forest through a stacking ensemble approach, the model achieves better performance. We have also developed a user-friendly web-based interface to showcase the model's practical applicability in real-world clinical settings, facilitating seamless integration into healthcare workflows.