Investigating demographic influences for HIV classification using bayesian autoassociative neural networks

This paper presents a method of determining whether demographic properties such as education, race, age, physical location, gravidity and parity influence the ability to classify the HIV status of a patient. The degree to which these variables influence the HIV classification is investigated by using an ensemble of autoassociative neural networks that are trained using the Bayesian framework. The HIV classification is treated as a missing data problem and the ensemble of autoassociative neural networks coupled with an optimization technique are used to determine a set of possible estimates. The set of possible estimates are aggregated together to give a predictive certainty measure. This measure is the percentage of the most likely estimate from all possible estimates. Changes to the state of each of the demographic properties are made and changes in the predictive certainty are recorded. It was found that the education level and the race of the patients are influential on the predictability of the HIV status. Significant knowledge discovery about the demographic influences on predicting a patients HIV status is obtained by the methods presented in this paper.