Regularized Neural Network to Identify Potential Breast Cancer: A Bayesian Approach
Hansapani Rodrigo, Chris P. Tsokos, Taysseer Sharaf · Journal of Modern Applied Statistical Methods · 2016
In the current study, we have exemplified the use of Bayesian neural networks for breast cancer classification using the evidence procedure. The optimal Bayesian network has 81% overall accuracy in correctly classifying the true status of breast cancer patients, 59% sensitivity in correctly detecting the malignancy and 83% specificity in correctly detecting the non-malignancy. The area under the receiver operating characteristic curve (0.7940) shows that this is a moderate classification model.