Neural networks are used in this chapter for classifying the HIV status of individuals based on socioeconomic and demographic characteristics. The trained network is then used to create an error equation with one of the demographic variables as a missing input and the desired HIV status as one of the variables. The missing variable thus becomes a control variable. This control mechanism is proposed to assess the effect of education level on the HIV risk of individuals and, thereby, assist in understanding the extent to which the spread of HIV can be controlled by using the education level. An inverse neural network model and a missing data approximation model based on autoassociative neural network and genetic algorithm (ANNGA) are used for the control mechanism. Therefore, the ANNGA is used to obtain the missing input values (education level) for the first model and an inverse neural network model is then used to obtain the missing input values (education) for the second model. The two models are then compared and it is found that the proposed inverse neural network model outperforms the ANNGA model. The methodology thus shows that HIV spread can be controlled to some extent by modifying a demographic characteristic educational level.