| Abstract |
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The preoperative discrimination between malignant and benign masses is a crucial issue in gynecology. Next to the large amount of background, knowledge there is a growing number of collected patient data that can be used in inductive techniques. These two sources of information result in two different modeling strategies. Based on the background knowledge various discrimination models are constructed by leading experts in the field, tuned and tested by observations. Based on the observations various statistical models are developed such as logistic regression models and artificial neural network models. For the efficient combination of prior background knowledge and observations, Bayesian network models were 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.
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 Additional Information
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Citation:
P. Antal, H. Verrelst, D. Timmerman, S. Van Huffel, B. de Moor, I. Vergote,
"Bayesian Networks in Ovarian Cancer Diagnosis: Potentials and Limitations,"
cbms,
p. 103,
13th IEEE Symposium on Computer-Based Medical Systems (CBMS'00),
2000
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