Robust breast cancer classification via ensemble learning: Integrating feature analysis and error evaluation techniques

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, highlighting the need for accurate and reliable computer-aided diagnostic methods. This study evaluates the effectiveness of ensemble learning techniques for classifying breast tumors as benign or malignant using clinical tumor features. A data-driven model was developed using a publicly available Kaggle breast cancer dataset. Four ensemble learning models, Random Forest, Gradient Boosting, AdaBoost, and Extra Trees, were trained and comparatively evaluated using accuracy, precision, recall, F1-score, and Receiver Operating Characteristic Area Under the Curve (ROC-AUC). Feature importance and error analyses were also conducted to identify influential predictors and assess model reliability. All evaluated models achieved excellent predictive performance, with classification accuracies above 96% and ROC-AUC values exceeding 0.99. The Extra Trees classifier demonstrated the best overall performance, achieving the highest accuracy, the lowest error rate, and superior sensitivity in detecting malignant tumors. Tumor characteristics related to perimeter, radius, and concave points were identified as the most influential features. Ensemble learning provides a robust and reliable approach for breast cancer classification, enabling highly accurate diagnostic predictions. The proposed model can support clinicians in early breast cancer detection and decision-making, potentially improving diagnostic accuracy and patient outcomes while reducing misclassification of malignant cases.