Predicting HIV Status Using Neural Networks and Demographic Factors

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dc.contributor.authorTim, Taryn Nicole Ho
dc.date.accessioned2007-02-15T12:27:23Z
dc.date.available2007-02-15T12:27:23Z
dc.date.issued2007-02-15T12:27:23Z
dc.descriptionStudent Number : 0006036T - MSc(Eng) project report - School of Electrical and Information Engineering - Faculty of Engineering and the Built Environmenten
dc.description.abstractDemographic 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.en
dc.format.extent726529 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/10539/2010
dc.language.isoenen
dc.subjectneural networksen
dc.subjectrisk assessmenten
dc.subjectHIVen
dc.subjectmultilayer perceptronen
dc.subjectmissing dataen
dc.titlePredicting HIV Status Using Neural Networks and Demographic Factorsen
dc.typeThesisen
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