Abstract
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.
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References
Timmerman, D., Verrelst, H., Bourne, T.H., De Moor, B., Collins, W.P., Vergote, I., Vandewalle, J.: Artificial neural network models for the preoperative discrimination between malignant and benign adnexal masses. Ultrasound Obstet Gynecol 13, 17–25 (1999)
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)
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)
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)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, Heidelberg (1995)
Suykens, J.A.K., Vandewalle, J.: Least squares support vector machine classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)
MacKay, D.J.C.: The evidence framework applied to classification networks. Neural Computation 4(5), 698–741 (1992)
Van Gestel, T., Suykens, J.A.K., Lanckriet, G., Lambrechts, A., De Moor, B., Vandewalle, J.: A Bayesian framework for Least Squares Support Vector Machine classifiers, Gaussian processes and kernel Fisher discriminant analysis. Neural Computation 15(5), 1115–1148 (2002)
Hanley, J.A., McNeil, B.: The meaning and use of the area under a Receiver Operating Characteristic (ROC) curve. Radiology 143, 29–36 (1982)
Jeffreys, H.: Theory of Probability. Oxford University Press, New York (1961)
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Lu, C., Van-Gestel, T., Suykens, J.A.K., Van-Huffel, S., Timmerman, D., Vergote, I. (2003). Classification of Ovarian Tumors Using Bayesian Least Squares Support Vector Machines. In: Dojat, M., Keravnou, E.T., Barahona, P. (eds) Artificial Intelligence in Medicine. AIME 2003. Lecture Notes in Computer Science(), vol 2780. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39907-0_31
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DOI: https://doi.org/10.1007/978-3-540-39907-0_31
Publisher Name: Springer, Berlin, Heidelberg
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