Abstract

Prediction of breast cancer based upon several features computed for each subject is a binary classification problem. Several discriminant methods exist for this problem, some of the commonly used methods are: Decision Trees, Random Forest, Neural Network, Support Vector Machine (SVM), and Logistic Regression (LR). Except for Logistic Regression, the other listed methods are predictive in nature; LR yields an explanatory model that can also be used for prediction, and for this reason it is commonly used in many disciplines including clinical research. In this article, we demonstrate the method of Bayesian LR to predict breast cancer using the Wisconsin Diagnosis Breast Cancer (WDBC) data set available at the UCI Machine Learning Repository.