Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines

Authors:
C. Lu;T. Van Gestel;J. A. K. Suykens;S. Van Huffel;I. Vergote;D. Timmerman
Affiliations:
Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium;Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium;Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium;Department of Electrical Engineering, ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium;Department of Obstetrics and Gynecology, University Hospitals KU Leuven, Herestraat 49, B-3000 Leuven, Belgium;Department of Obstetrics and Gynecology, University Hospitals KU Leuven, Herestraat 49, B-3000 Leuven, Belgium
Venue:
Artificial Intelligence in Medicine
Year:
2003

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Abstract

In this work, we develop and evaluate several least squares support vector machine (LS-SVM) classifiers within the Bayesian evidence framework, in order to preoperatively predict malignancy of ovarian tumors. The analysis includes exploratory data analysis, optimal input variable selection, parameter estimation, and performance evaluation via receiver operating characteristic (ROC) curve analysis. LS-SVM models with linear and radial basis function (RBF) kernels, and logistic regression models have been built on 265 training data, and tested on 160 newly collected patient data. The LS-SVM model with nonlinear RBF kernel achieves the best performance, on the test set with the area under the ROC curve (AUC), sensitivity and specificity equal to 0.92, 81.5% and 84.0%, respectively. The best averaged performance over 30 runs of randomized cross-validation is also obtained by an LS-SVM RBF model, with AUC, sensitivity and specificity equal to 0.94, 90.0% and 80.6%, respectively. These results show that the LS-SVM models have the potential to obtain a reliable preoperative distinction between benign and malignant ovarian tumors, and to assist the clinicians for making a correct diagnosis.