well as conventional feed-forward neural networks to estimate HIV risk from demographic factors. Although good accuracy was achieved when using the autoencoder method, it is disadvantageous due to its "black box" nature which is that it is not transparent. To improve transparency Bayesian rough set theory (RST) was proposed to forecast and interpret the causal effects of HIV (Marwala and Crossingham, 2007) and good accuracy and relevant rules that govern the relationships between demographic characteristics and HIV were identified. Rough sets have been used in various biomedical and engineering applications (Ohrn, 1999; Pe-a et. al, 1999; Tay and Shen, 2003; Golan and Ziarko, 1995). But in most applications, RST is used primarily for prediction. 

Rowland et. al. (1998) compared the use of RST and neural networks for the prediction of ambulation spinal cord injury, and although the neural network method produced more accurate results, its "black box" nature makes it impractical for the use of rule extraction problems. Poundstone et. al. (2004) related demographic properties to the spread of HIV. 

In their work they justified the use of demographic properties to create a model to predict HIV from a given database. The data set used in this paper was obtained from the South African antenatal sero-prevalence survey of 2001 (Department of Health, 2001). The data was obtained through questionnaires completed by pregnant women attending selected public clinics and was conducted concurrently across all nine provinces in South Africa. 

The six demographic variables considered are: race, age of mother, education, gravidity, parity and, age of father, with the outcome or decision being either HIV positive or negative. The HIV status is the decision represented in binary form as either a 0 or 1, with a 0 representing HIV negative and a 1 representing HIV positive. The input data was discretised into four partitions. This number was chosen as it gave a good balance 