Bayesian networks in ovarian cancer diagnosis: potentials and limitations

The pre-operative discrimination between malignant and benign masses is a crucial issue in gynaecology. Next to the large amount of background knowledge, there is a growing amount of collected patient data that can be used in inductive techniques. These two sources of information result in two diffe...

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Bibliographic Details
Main Authors: Antal, P., Verrelst, H., Timmerman, D., Moreau, Y., Van Huffel, S., De Moor, B., Vergote, I.
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Bayesian methods
Cancer
Gynaecology
Information resources
Intelligent networks
Logistics
Medical diagnostic imaging
Medical treatment
Testing
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Summary:The pre-operative discrimination between malignant and benign masses is a crucial issue in gynaecology. Next to the large amount of background knowledge, there is a growing amount of collected patient data that can be used in inductive techniques. These two sources of information result in two different modelling strategies. Based on the background knowledge, various discrimination models have been constructed by leading experts in the field, tuned and tested by observations. Based on the patient observations, various statistical models have been developed, such as logistic regression models and artificial neural network models. For the efficient combination of prior background knowledge and observations, Bayesian network models are suggested. We summarize the applicability of this technique, report the performance of such models in ovarian cancer diagnosis and outline a possible hybrid usage of this technique.
ISSN:1063-7125
DOI:10.1109/CBMS.2000.856886