"Enhancing Breast Cancer Detection with Ensemble Methods: A Comprehensive Analysis"

Abstract

The study delves into the intricate analysis of breast cancer, employing four powerful machine learning algorithms: k-Nearest Neighbors (KNN), Support Vector Machines (SVM), Naive Bayes, and Random Forest. To further enhance the pre- dictive performance, an  ensemble  method  harnessing  XGBoost isutilized. The dataset comprises an array of clinical and histological features extracted from breast cancer patients. The team applies cutting-edge preprocessing techniques to address missing values, normalize features, and tackle class imbalance issues. The results reveal the sheer efficacy of  KNN,  SVM, Naive Bayes, and Random  Forest algorithms in breast cancer  analysis.  The ensemble method,  with its ability to amalgamatethe predictions of multiple models, brings forth an outcome that is not only precise but also resilient. A feature importance analysis is conducted using the ensemble method, revealing the most significant features that play a vital role in breast cancer prediction. The findings are a testament to the rapid progress in machine learning research for  breast  cancer  analysis and  openup new avenues for further advancement in this crucial  field.