Regularized Neural Network to Identify Potential Breast Cancer: A Bayesian Approach

Authors

Hansapani S. Rodrigo, University of South FloridaFollow
Chris P. Tsokos, University of South FloridaFollow
Taysseer Sharaf, University of Michigan-DearbornFollow

Abstract

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.

DOI

10.22237/jmasm/1478003520