Learning in graphical models
Expert Systems and Probabiistic Network Models
Expert Systems and Probabiistic Network Models
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Learning the Topological Properties of Brain Tumors
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Ovarian cancer diagnosis with complementary learning fuzzy neural network
Artificial Intelligence in Medicine
Classification of Otoneurological Cases According to Bayesian Probabilistic Models
Journal of Medical Systems
Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
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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.