onr:"swepub:oai:lup.lub.lu.se:2e8b93df-2207-4bd1-8873-fbbe9de5fa43"
Search: onr:"swepub:oai:lup.lub.lu.se:2e8b93df-2207-4bd1-8873-fbbe9de5fa43" > Using Bayesian neur...
- 1 of 1
- Previous record
- Next record
- To hitlist
- Van Calster, Ben (author)
Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery
- Article/chapterEnglish2008
Publisher, publication year, extent ...
- 2007-09-04
- Springer Science and Business Media LLC,2008
Numbers
- LIBRIS-ID:oai:lup.lub.lu.se:2e8b93df-2207-4bd1-8873-fbbe9de5fa43
- https://lup.lub.lu.se/record/1286605URI
- https://doi.org/10.1007/s00521-007-0147-1DOI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- 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.
Subject headings and genre
- MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Reproduktionsmedicin och gynekologi hsv//swe
- MEDICAL AND HEALTH SCIENCES Clinical Medicine Obstetrics, Gynaecology and Reproductive Medicine hsv//eng
- ultrasound
- automatic relevance determination
- netlab
- evidence framework
- Bayesian
- ovarian tumour classification
- multi-layer perceptrons
Added entries (persons, corporate bodies, meetings, titles ...)
- Timmerman, Dirk (author)
- Nabney, Ian T. (author)
- Valentin, LilLund University,Lunds universitet,Obstetrisk, gynekologisk och prenatal ultraljudsdiagnostik,Forskargrupper vid Lunds universitet,Obstetric, Gynaecological and Prenatal Ultrasound Research,Lund University Research Groups(Swepub:lu)obst-lva (author)
- Testa, Antonia C. (author)
- Van Holsbeke, Caroline (author)
- Vergote, Ignace (author)
- Van Huffel, Sabine (author)
- Obstetrisk, gynekologisk och prenatal ultraljudsdiagnostikForskargrupper vid Lunds universitet (creator_code:org_t)
Related titles
- In:NEURAL COMPUTING & APPLICATIONS: Springer Science and Business Media LLC17:5-6, s. 489-5000941-06431433-3058
Internet link
Find in a library
- NEURAL COMPUTING & APPLICATIONS (Search for host publication in LIBRIS)
To the university's database
- 1 of 1
- Previous record
- Next record
- To hitlist
Find more in SwePub
- By the author/editor
- Van Calster, Ben
- Timmerman, Dirk
- Nabney, Ian T.
- Valentin, Lil
- Testa, Antonia C ...
- Van Holsbeke, Ca ...
- show more...
- Vergote, Ignace
- Van Huffel, Sabi ...
- show less...
- About the subject
-
- MEDICAL AND HEALTH SCIENCES
- MEDICAL AND HEAL ...
- and Clinical Medicin ...
- and Obstetrics Gynae ...
- Articles in the publication
- NEURAL COMPUTING ...
- By the university
- Lund University
Search outside SwePub
- Extend your search to:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - National Library Systems
- LIBRIS.kb.se
