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

• 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.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Reproduktionsmedicin och gynekologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Obstetrics, Gynaecology and Reproductive Medicine (hsv//eng)

Nyckelord

ultrasound

automatic relevance determination

netlab

evidence framework

Bayesian

ovarian tumour classification

multi-layer perceptrons

Publikations- och innehållstyp

kon (ämneskategori)

ref (ämneskategori)