Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery

Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Antonia C. Testa, Caroline Van Holsbeke, Ignace Vergote, Sabine Van Huffel

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review

Abstract

In this paper, we applied Bayesian multi-layer perceptrons (MLP) using the evidence procedure to predict malignancy of ovarian masses in a large (n = 1,066) multi-centre data set. Automatic relevance determination (ARD) was used to select the most relevant inputs. Fivefold cross-validation (5CV) and repeated 5CV was used to select the optimal combination of input set and number of hidden neurons. Results indicate good performance of the models with area under the receiver operating characteristic curve values of 0.93-0.94 on independent data. Comparison with a linear benchmark model and a previously developed logistic regression model shows that the present problem is very well linearly separable. A resampling analysis further shows that the number of hidden neurons specified in the ARD analyses for input selection may influence model performance. This paper shows that Bayesian MLPs, although not frequently used, are a useful tool for detecting malignant ovarian tumours.
Original languageEnglish
Title of host publicationNEURAL COMPUTING & APPLICATIONS
PublisherSpringer
Pages489-500
Volume17
DOIs
Publication statusPublished - 2008
Event28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society - New York, NY
Duration: 2006 Aug 302006 Sept 3

Publication series

Name
Number5-6
Volume17
ISSN (Print)0941-0643

Conference

Conference28th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society
Period2006/08/302006/09/03

Subject classification (UKÄ)

  • Obstetrics, Gynecology and Reproductive Medicine

Free keywords

  • ultrasound
  • automatic relevance determination
  • netlab
  • evidence framework
  • Bayesian
  • ovarian tumour classification
  • multi-layer perceptrons

Fingerprint

Dive into the research topics of 'Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery'. Together they form a unique fingerprint.

Cite this