An Advanced Stacking-based Machine Learning and Deep Learning Framework for Breast Cancer Prediction

Breast cancer remains a critical global health challenge with early detection being vital for improving patient outcomes. Traditional diagnostic methods may be time-consuming and resource-intensive, highlighting the need for efficient machine learning solutions. This study addresses this need by developing a robust machine learning framework for breast cancer prediction using the Wisconsin Breast Cancer Diagnostic dataset. We implement a comprehensive preprocessing pipeline, intelligent feature selection, and rigorous comparative evaluation of seven advanced ML models including XGBoost, Neural Networks, and ensemble methods. Our evaluation prioritized both classification accuracy and computational efficiency, explicitly measuring model training and inference time. Results demonstrated exceptional performance with the SGD Classifier achieving the highest test accuracy of $\mathbf{98.25\%}$, while XGBoost, AdaBoost, and SVM RBF Optimized achieved $\mathbf{97.37\%}$ accuracy. The SGD Classifier demonstrated superior computational efficiency, achieving peak performance with a training time of only \textbf{0.05 seconds}, making it significantly faster than other high-performing models. We deployed an interactive Streamlit web application for real-time prediction, bridging the gap between research and clinical practice. This work provides a highly accurate, scalable, and efficient solution for early breast cancer diagnosis, with the code available on our \href{https://github.com/Ibtasam-98/breast-cancer-prediction}{GitHub repository}. Supplementary Material File (breast_cancer_prediction (6).pdf) - Download - 1.92 MB Information & Authors Information Version history Copyright This work is licensed under a Non Exclusive No Reuse License.

Authors Metrics & Citations Metrics Article Usage 157views 79downloads Citations Download citation Ibtasam Ur Rehman, Muhammad Islam, Basharat Hussain. An Advanced Stacking-based Machine Learning and Deep Learning Framework for Breast Cancer Prediction. Authorea. 02 February 2026. DOI: https://doi.org/10.22541/au.177001054.47334958/v1 DOI: https://doi.org/10.22541/au.177001054.47334958/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu.