Using artificial neural networks to predict malignancy of ovarian tumors

This paper discusses the application of artificial neural networks (ANNs) to preoperative discrimination between benign and malignant ovarian tumors. With the input variables selected by logistic regression analysis, two types of feed-forward neural networks were built: multi-layer perceptrons (MLPs) and generalized regression networks (GRNNs). We assess the performance of the models using the Receiver Operating Characteristic (ROC) curve, particularly the area under the ROC curves (AUC), and statistically compare the cross-validated estimate of the AUC of different models.