Design, construction and evaluation of systems to predict risk
in obstetrics
D. R. Lovell, B. Rosario, M. Niranjan, R. W. Prager,
K. J. Dalton, R. Derom and J. Chalmers
We present a systematic, practical approach to developing risk
prediction systems, suitable for use with large databases of medical
information. An important part of this approach is a novel feature
selection algorithm which uses the area under the receiver operating
characteristic (ROC) curve to measure the expected discriminative
power of different sets of predictor variables. We describe this
algorithm and use it to select variables to predict risk of a specific
adverse pregnancy outcome: failure to progress in labour. Neural
network, logistic regression and hierarchical Bayesian risk prediction
models are constructed, all of which achieve close to the limit of
performance attainable on this prediction task. We show that better
prediction performance requires more discriminative clinical
information rather than improved modelling techniques. It is also
shown that better diagnostic criteria in clinical records would
greatly assist the development of systems to predict risk in
pregnancy.
Keywords: Receiver operating characteristic (ROC); Feature selection;
Risk prediction in pregnancy; Failure to progress; Neural networks.