Neuro-rough models for modelling HIV
Tshilidzi Marwala, Bodie Crossingham · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · 2008
This paper proposes a neuro-rough model based on multi-layered perceptron (MLP) and rough set theory. The neuro-rough model is then tested on modeling the risk of HIV (human immunodeficiency virus) from demographic data. The model is formulated using Bayesian framework and trained using Monte Carlo method and Metropolis criterion. When the model was tested to estimate the risk of HIV infection given the demographic data it was found to give the accuracy of 62%. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model.