Construction of a Bayesian network for mammographic diagnosis of breast cancer

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. Mammo...

Full description

Saved in:
Bibliographic Details
Published in:Computers in biology and medicine 1997, Vol.27 (1), p.19-29
Main Authors: Kahn, Charles E., Roberts, Linda M., Shaffer, Katherine A., Haddawy, Peter
Format: Article
Language:English
Subjects:
Artificial intelligence
Bayes Theorem
Bayesian networks
Biological and medical sciences
Breast cancer
Breast Neoplasms - diagnosis
Computer-aided diagnosis
Computerized, statistical medical data processing and models in biomedicine
Data Collection
Diagnosis, Computer-Assisted
Expert systems
Female
Humans
Mammography
Mammography - methods
Medical management aid. Diagnosis aid
Medical sciences
Middle Aged
Neural Networks (Computer)
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0010-4825
1879-0534
DOI:10.1016/S0010-4825(96)00039-X