Enhanced Breast Cancer Relapse Prediction using Ensemble Learning Techniques

Breast cancer remains a significant health concern for women, and accurate prediction of recurrence is vital for effective treatment and improved outcomes. This research investigates the application of advanced ensemble learning techniques to enhance the reliability of breast cancer recurrence predictions. By combining Deep Neural Networks (DNN) and Artificial Neural Networks (ANN) with traditional machine learning methods, the proposed approach demonstrates significant improvements in accuracy, precision, sensitivity, specificity, F1-scores, and areas under the curve (AVCs). The study utilizes two breast cancer relapse datasets, UMCIO and WPBC, to evaluate the performance of the ensemble learning models. The results highlight the effectiveness of the proposed approach in accurately predicting breast cancer recurrence. This research contributes to the advancement of breast cancer diagnosis and treatment by providing a valuable tool for clinicians to assess the risk of recurrence and tailor treatment plans accordingly.