| Summary: | In this study, a new method to analyze HIV using a combination of autoencoder
networks and genetic algorithms is proposed. The proposed method is
tested on a set of demographic properties of individuals obtained from the
South African antenatal survey. The autoencoder model is then compared
with a conventional feedforward neural network model and yields a classification
accuracy of 92% compared to 84% obtained for the conventional feedforward
model. The autoencoder model is then used to propose a new method
of approximating missing entries in the HIV database using ant colony optimization.
This method is able to estimate missing input to an accuracy of
80%. The estimated missing input values are then used to analyze HIV. The
autoencoder network classifier model yields a classification accuracy of 81% in
the presence of missing input values. The feedforward neural network classifier
model yields a classification accuracy of 82% in the presence of missing input
values. A control mechanism is proposed to assess the effect of demographic
properties on the HIV status of individuals, based on inverse neural networks,
and autoencoder networks-based-on-genetic algorithms. This control mechanism
is aimed at understanding whether HIV susceptibility can be controlled
by modifying some of the demographic properties. The inverse neural network
control model has accuracies of 77% and 82%, meanwhile the genetic algorithm
model has accuracies of 77% and 92%, for the prediction of educational level
of individuals, and gravidity, respectively. HIV modelling using neuro-fuzzy
models is then investigated, and rules are extracted, which provide more valuable
insight. The classification accuracy obtained by the neuro-fuzzy model
is 86%. A rough set approximation is then investigated for rule extraction,
and it is found that the rules present simplistic and understandable relationships
on how the demographic properties affect HIV risk. The study concludes
by investigating a model for automatic relevance determination, to determine
which of the demographic properties is important for HIV modelling. A comparison
is done between using the full input data set and the data set using the
input parameters selected by the technique for the HIV classification. Age of
the individual, gravidity, province, region, reported pregnancy and educational
level were amongst the input parameters selected as relevant for classification
of an individual’s HIV risk. This study thus proposes models, which can be
used to understand HIV dynamics, and can be used by policy-makers to more
effectively understand the demographic influences driving HIV infection.
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