Samer M. Sharfo

Spearman-guided XGBoost Classification for Breast Cancer Diagnosis Using the Wisconsin Diagnostic Dataset

A comprehensive Machine Learning framework that employs k-Nearest Neighbors, Random Forest, Logistic Regression, and Extreme Gradient Boosting algorithms to analyze a breast cancer dataset obtained from the UCI repository underscores the potential of ML-driven approaches in enhancing breast cancer diagnostics and contr...