Toward Improving Breast Cancer Classification Using an Adaptive Voting Ensemble Learning Algorithm

Breast cancer remains one of the most prevalent and life-threatening diseases among women worldwide. Accurate and early diagnosis is crucial for effective treatment and improved survival rates. This project proposes an advanced breast cancer classification system using an adaptive voting ensemble learning algorithm. The proposed method integrates Decision Tree and Random Forest classifiers, leveraging their strengths to create a more accurate and robust model. By using a weighted voting mechanism, the ensemble model dynamically adapts to the performance of individual classifiers, ensuring that more accurate models have greater influence on the final prediction. The system is trained and evaluated on the Breast Cancer Wisconsin (Diagnostic) Dataset from the UCI Machine Learning Repository. The effectiveness of the proposed model is assessed using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The anticipated outcome is an improved breast cancer classification system that enhances diagnostic accuracy and reliability, aiding in early detection and treatment planning, and ultimately contributing to better patient outcomes.

Keywords: Breast Cancer, Adaptive Voting Ensemble Learning Algorithm, Random Forest, decision tree.