ITEM METADATA RECORD
Title: Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: A multi-center study
Authors: Van Calster, Ben ×
Timmerman, Dirk
Nabney, I.T
Valentin, L
Van Holsbeke, C
Van Huffel, Sabine #
Issue Date: 2006
Publisher: IEEE
Host Document: Proc. of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2006) pages:5342-5345
Conference: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2006) location:New York City, USA date:Sep. 2006
Abstract: Ovarian masses are common and a good pre-surgical assessment of their nature is important for adequate treatment. Bayesian Multi-Layer Perceptrons (MLPs) using the evidence procedure were used to predict whether tumors are malignant or not. Automatic Relevance Determination (ARD) is used to select the most relevant of the 40+ available variables. Cross-validation is used to select an optimal combination of input set and number of hidden neurons. The data set consists of 1066 tumors collected at nine centers across Europe. Results indicate good performance of the models with AUC values of 0.93-0.94 on independent data. A comparison with a Bayesian perceptron model shows that the present problem is to a large extent linearly separable. The analyses further show that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance.
Description: \emph{Proc. of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC2006)}, New York City, USA, Sep. 2006
URI: 
ISSN: 1557-170X
Publication status: published
KU Leuven publication type: IC
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Gynaecological Imaging Section (-)
Section Woman - Miscellaneous (-)
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy

 




All items in Lirias are protected by copyright, with all rights reserved.

© Web of science