Enhanced Prediction of Breast Cancer Using Machine Learning Ensemble Models and Techniques

Breast cancer was acknowledged as one of the world’s most formidable diseases. The key to improving patient outcomes and general wellbeing had been to make an accurate and timely diagnosis. This research used Machine Learning methods to investigate several ensemble approaches to breast cancer diagnosis prediction. This research made extensive use of the Breast Cancer Wisconsin dataset. The research aimed to provide a comprehensive evaluation of the predictive powers of a variety of five ensemble models, including Random Forest, Gradient Boosting, AdaBoost, Bagging, and Extra Trees. The approach included several criteria for assessment, including accuracy, precision, recall, and F1-score. Further analysis was done with the use of the ROC curve, the precision-recall curve, and other statistical tools. It’s important to note that among all the models tested, AdaBoost performed the best.