Supporting Healthcare Professionals with Machine Learning for Early Detection of Breast Cancer

Early detection of breast cancer is crucial for improving survival rates and patient outcomes. However, accurately identifying the early signs of breast cancer remains a significant challenge for healthcare professionals, often due to the subtlety of early-stage indicators and the complexity of interpreting medical data. In this paper, we propose a comprehensive AI-driven approach to assist healthcare professionals in the early detection of breast cancer. Comparing both Machine Learning (ML) and Deep Learning (DL) techniques, we conduct an extensive evaluation of several models, using the Wisconsin Breast Cancer dataset. Our analysis reveals that ensemble learning models, particularly Random Forest (RF) and Adaptive Boosting (AB), demonstrate superior performance, with both models achieving an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 8. 2 \%}$</tex> and an Area Under the Curve of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0. 9 9}$</tex>. These results underscore the potential of these models to accurately predict breast cancer, thereby helping in early detection.