Predicting HIV Status Using Neural Networks and Demographic Factors
Date
2007-02-15T12:27:23Z
Authors
Tim, Taryn Nicole Ho
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Abstract
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 classifier: a neural network trained to perform binary classification,
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 classification performance is measured, and the sensitivity and specificity
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
effect of the imputed data on the network prediction is investigated.
Description
Student Number : 0006036T -
MSc(Eng) project report -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built Environment
Keywords
neural networks, risk assessment, HIV, multilayer perceptron, missing data