Classifying ovarian tumors using Bayesian Multi-Layer Perceptrons and Automatic Relevance Determination: a multi-center study.
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.
Van Calster
Ben
B
Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium. bvancals@esat.kuleuven.be
Timmerman
Dirk
D
Nabney
Ian T
IT
Valentin
Lil
L
Van Holsbeke
Caroline
C
Van Huffel
Sabine
S
D000465:Algorithms; D019540:Area Under Curve; D001331:Automation; D001499:Bayes Theorem; D005060:Europe; D005260:Female; D006801:Humans; D015233:Models, Statistical; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software