Supervised Machine Learning for Breast Cancer Risk Factors Analysis and Survival Prediction

Abstract

The choice of the most effective treatment may eventually be influenced by the breast cancer survival prediction. For the purpose of predicting 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 model 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%.