magnitudes of the γ **which is the output of the rough set model, it was found that the** rough set model contributes (average γ **of 0.43) to the neuro-rough set model less than** 
the neural network component (average w **of 0.49). The receiver operator characteristics** (ROC) curve was also generated and the area under the curve was calculated to be 0.59 and is shown in Figure 4. This shows that the neuro-rough model proposed is able to estimate the HIV status. 

![0_image_0.png](0_image_0.png)

Training neuro-rough model with Bayesian framework allows us to determine how confident we are on the HIV status we predict. For example, Figure 5 shows that the average HIV status predicted is 0.8 indicating that a person is probably HIV positive. 