title: Construction of a Bayesian network for mammographic diagnosis of breast cancer.
abstract: Bayesian networks use the techniques of probability theory to reason under uncertainty, and have become an important formalism for medical decision support systems. We describe the development and validation of a Bayesian network (MammoNet) to assist in mammographic diagnosis of breast cancer. MammoNet integrates five patient-history features, two physical findings, and 15 mammographic features extracted by experienced radiologists to determine the probability of malignancy. We outline the methods and issues in the system's design, implementation, and evaluation. Bayesian networks provide a potentially useful tool for mammographic decision support.
authors.affiliation: Department of Radiology, Medical College of Wisconsin, Milwaukee 53226, USA.
authors.forename: C E
authors.initials: CE
authors.lastname: Kahn
authors.forename: L M
authors.initials: LM
authors.lastname: Roberts
authors.forename: K A
authors.initials: KA
authors.lastname: Shaffer
authors.forename: P
authors.initials: P
authors.lastname: Haddawy
mesh_terms: D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000175:diagnosis*; D003625:Data Collection; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012984:Software