framework is then applied to modeling the relationships between demographic properties and the risk of HIV.

## Rough Set Theory (Rst)

Rough set theory deals with the approximation of sets that are difficult to describe with the available information (Orhn and Rowland, 2006). It deals predominantly with the classification of imprecise, uncertain or incomplete information. Some concepts that are fundamental to RST theory are given in the next few sections. The data is represented using an information table and an example for the HIV data set for the ith **object is given** 
in Table 1: 

|        | Race   | Mothers'   | Education   | Gravidity   | Parity   | Fathers'   | HIV   |
|--------|--------|------------|-------------|-------------|----------|------------|-------|
| Age    | Age    | Status     |             |             |          |            |       |
| Obj(1) | 1      | 32         | 1           | 1           | 2        | 34         | 0     |
| Obj(2) | 2      | 27         | 13          | 2           | 1        | 28         | 1     |
| Obj(3) | 2      | 25         | 8           | 2           | 0        | 23         | 1     |
| .      | .      | .          | .           | .           | .        | .          | .     |
| Obj(i) | 3      | 27         | 4           | 3           | 1        | 22         | 0     |

In the information table, each row represents a new case (or object). Besides HIV status, each of the columns represents the respective case's variables (or condition attributes). The HIV Status is the outcome (also called the concept or decision attribute) of each object. The outcome contains either a 1 or 0, and this indicates whether the particular case is infected with HIV or not. 