Using Bayesian neural networks with ARD input selection to detect malignant ovarian masses prior to surgery |
| |
Authors: | Ben Van Calster Dirk Timmerman Ian T Nabney Lil Valentin Antonia C Testa Caroline Van Holsbeke Ignace Vergote Sabine Van Huffel |
| |
Affiliation: | 1. Department of Electrical Engineering (ESAT-SCD), Katholieke Universiteit Leuven, Kasteelpark Arenberg 10, 3001, Leuven, Belgium 2. Department of Obstetrics and Gynaecology, University Hospitals Leuven, Herestraat 49, 3000, Leuven, Belgium 3. Neural Computing Research Group (NCRG), Aston University, Aston Triangle, Birmingham, B4 7ET, UK 4. Department of Obstetrics and Gynaecology, Malm? University Hospital, 205 02, Malm?, Sweden 5. Gynecologic Oncology Unit, Università Cattolica del Sacro Cuore, Largo Agostino Gemelli 8, 00168, Rome, Italy
|
| |
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. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|