This study compares Random Forest, Logistic Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and XGBOOST on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset by calculating their classification test accuracy, sensitivity, and specificity. The dataset was divided using a K-fold cross-check in the following way: four copies of training data and one copy of test data for testing and all the classifiers' parameters were assigned. Results show that the best classifier was the Logistic Regression model with an average auc value close to 1, followed by the Random Forest models, while XGBOOST was the poorest model.