Development of a Bayesian Network for Diagnosis of Breast Cancer

Linda M. Roberts, Charles E. Kahn, Jr., Peter Haddawy Department of Electrical Engineering and Computer Science University of Wisconsin­Milwaukee Milwaukee, Wisconsin 53201 USA


We describe the early stages of the development and validation of a Bayesian network to assist in the detection of breast cancer. MammoNet integrates mammo­ graphic findings, demographic factors, and physical examination to determine the probability of malignancy. Conditional probabilities were obtained from the medical literature and from expert opinion. Problems (and solutions) encountered while developing the model are discussed. MammoNet is imple­ mented as a knowledge base of rules; problem­specific networks are constructed using a Bayesian network construction algorithm. The model's performance was evaluated with 77 cases drawn from a textbook and a clinical teaching file; MammoNet performed well, and achieved an area under the receiver operating curve of 0.881 (± 0.045).