Title: Preoperative prediction of malignancy of ovarian tumors using least squares support vector machines
Authors: Lu, Chuan ×
Van Gestel, Tony
Suykens, Johan
Van Huffel, Sabine
Vergote, Ignace
Timmerman, Dirk #
Issue Date: Jul-2003
Series Title: Artificial intelligence in medicine vol:28 issue:3 pages:281-306
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.
ISSN: 0933-3657
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Gynaecological Oncology
Basic Research in Gynaecology Section (-)
× corresponding author
# (joint) last author

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