Classifying ovarian tumors using Bayesian multi-layer perceptrons and automatic relevance determination: A multi-center study

Ben Van Calster, Dirk Timmerman, Ian T. Nabney, Lil Valentin, Caroline Van Holsbeke, Sabine Van Huffel

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

10 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages5342-5345
Number of pages4
ISBN (Print)1424400325
DOIs
Publication statusPublished - 1 Dec 2006

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