Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines
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The aim of this study is to develop the Bayesian LeastSquares Support Vector Machine (LS-SVM) classifiers for preoperative discrimination between benign and malignant ovarian tumors. We describe how to perform (hyper)parameter estimation, input variable selection for LS-SVMs within the evidence framework. The issue of computing the posterior class probability for risk minimization decision making is addressed. The performance of the LS-SVM models with linear and RBF kernels has been evaluated and compared with Bayesian multi-layer perceptrons (MLPs) and linear discriminant analysis.
KeywordsSupport Vector Machine Ovarian Tumor Little Square Support Vector Machine Malignant Ovarian Tumor Model Evidence
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- 2.Lu, C., De Brabanter, J., Van Huffel, S., Vergote, I., Timmerman, D.: Using artificial neural networks to predict malignancy of ovarian tumors. In: Proc. 23rd Annu. Int. Conf. of the IEEE Engineering in Medicine and Biology Society, Istanbul, Turkey, October 25-28, pp. 4.2.2-6 (2001)Google Scholar
- 3.Antal, P., Verrelst, H., Timmerman, D., Moreau, Y., Van Huffel, S., De Moor, B., Vergote, I.: Bayesian networks in ovarian cancer diagnosis: potentials and limitations. In: Proceeding of the 13th IEEE Symposium on Computer-Based Medical Systems (CBMS 2000), Houston, TX, pp. 103–109 (2000)Google Scholar
- 4.Timmerman, D., Valentin, L., Bourne, T.H., Collins, W.P., Verrelst, H., Vergote, I.: Terms, Definitions and Measurements to describe the ultrasonographic features of adnexal tumors: a consensus opinion from the international ovarian tumor analysis (IOTA) group. Ultrasound Obstet Gynecol 16, 500–505 (2000)CrossRefGoogle Scholar
- 8.Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)Google Scholar
- 11.Hanley, J.A., McNeil, B.: The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology 143, 29–36 (1982)Google Scholar