Breast Cancer Risk Prediction using XGBoost and Random Forest Algorithm
Sajib Kabiraj, M. Raihan, Nasif Alvi, Marina Afrin, Laboni Akter, Shawmi Akhter Sohagi, Etu Podder
Abstract
Breast cancer is as one of the common and serious cause of death among women globally. This is a disease where the cells grow out of control inside the breast. Family History of cancer disease, physical inactivity, psychological stress, increase in breast size are the risk factors of breast cancer. In this research paper, breast cancer dataset was analyzed to predict breast cancer using popular two ensemble machine learning algorithms. Random Forest and Extreme Gradient Boosting (XGBoost) were used to predict breast cancer. A total of 275 instances with 12 features were used for this analysis. With Random forest algorithm 74.73% accuracy and 73.63% using XGBoost had obtained in this analysis.