Synergizing Ensemble Techniques for Precision Breast Cancer Diagnosis: A Comparative Performance Analysis

Breast cancer presents a significant health challenge globally, demanding effective early detection and diagnosis for improved patient outcomes. Leveraging machine learning techniques offers promising avenues for enhancing diagnostic accuracy, with methods like machine learning demonstrating remarkable performance. However, to further enhance precision in breast cancer diagnosis, this study explores the effectiveness of ensemble learning methodologies. Evaluating ensemble models comprising various classifiers, including Random Forest, AdaBoost, Bagging, Gradient Boosting, Extreme Gradient Boosting (XGBoost), and Hist Gradient Boosting, the research aims to identify optimal configurations for accurately distinguishing between malignant and benign breast lesions. The experimental results reveal XGBoost as the optimal ensemble technique, with an impressive accuracy of 94.737%. Notably, XGBoost’s scalability, efficiency, and robustness, coupled with features like gradient boosting and regularization techniques, contribute to its exceptional predictive capability. This comprehensive analysis provides detailed insights into the specific strengths of ensemble techniques, highlighting their potential to significantly enhance breast cancer diagnosis and patient outcomes. The findings lay a solid foundation for better clinical decision-making in breast cancer management, with the potential for substantial improvements in healthcare practices.