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Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: A multi-center study

Van Calster, B ; Timmerman, D ; Nabney, I T ; Valentin, Lil LU ; Van Holsbeke, C and Van Huffel, S (2006) 1. p.5342-5345
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... (More)
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. (Less)
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publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE
volume
1
pages
3 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:34047133238
ISSN
1557-170X
DOI
10.1109/IEMBS.2006.260118
language
English
LU publication?
yes
id
8fcee309-7600-4607-8efe-428208a8160e (old id 1136639)
alternative location
http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=17945894
date added to LUP
2016-04-01 15:26:42
date last changed
2020-01-12 18:29:27
@inproceedings{8fcee309-7600-4607-8efe-428208a8160e,
  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.},
  author       = {Van Calster, B and Timmerman, D and Nabney, I T and Valentin, Lil and Van Holsbeke, C and Van Huffel, S},
  booktitle    = {Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE},
  issn         = {1557-170X},
  language     = {eng},
  pages        = {5342--5345},
  publisher    = {IEEE - Institute of Electrical and Electronics Engineers Inc.},
  title        = {Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: A multi-center study},
  url          = {http://dx.doi.org/10.1109/IEMBS.2006.260118},
  doi          = {10.1109/IEMBS.2006.260118},
  volume       = {1},
  year         = {2006},
}