  The choice of the most effective treatment may eventually be influenced by
breast cancer survival prediction. To predict the chances of a patient
surviving, a variety of techniques were employed, such as statistical, machine
learning, and deep learning models. In the current study, 1904 patient records
from the METABRIC dataset were utilized to predict a 5-year breast cancer
survival using a machine learning approach. In this study, we compare the
outcomes of seven classification models to evaluate how well they perform using
the following metrics: recall, AUC, confusion matrix, accuracy, precision,
false positive rate, and true positive rate. The findings demonstrate that the
classifiers for Logistic Regression (LR), Support Vector Machines (SVM),
Decision Tree (DT), Random Forest (RD), Extremely Randomized Trees (ET),
K-Nearest Neighbor (KNN), and Adaptive Boosting (AdaBoost) can accurately
predict the survival rate of the tested samples, which is 75,4\%, 74,7\%,
71,5\%, 75,5\%, 70,3\%, and 78 percent.
