Demographic and medical history information obtained from annual South African antenatal surveys is used to estimate the risk of acquiring HIV. The estimation system consists of a classi?er: a neural network trained to perform binary classi?cation, using supervised learning with the survey data. The survey information contains discrete variables such as age, gravidity and parity, as well as the quantitative variables race and location, making up the input to the neural network. HIV status is the output. A multilayer perceptron with a logistic function is trained with a cross entropy error function, providing a probabilistic interpretation of the output. Predictive and classi?cation performance is measured, and the sensitivity and speci?city are illustrated on the Receiver Operating Characteristic. An auto-associative neural network is trained on complete datasets, and when presented with partial data, global optimisation methods are used to approximate the missing entries. The e?ect of the imputed data on the network prediction is investigated.