Bayesian Approach to Neuro-Rough Models for Modelling HIV 
Tshilidzi Marwala and Bodie Crossingham University of the Witwatersrand Private Bag x3 

$${\mathrm{Wits}},\,2050$$

South Africa e-mail: t.marwala@ee.wits.ac.za This paper proposes a new neuro-rough model for modelling the risk of HIV from demographic data. The model is formulated using Bayesian framework and trained using Markov Chain 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% as opposed to 58% obtained from a Bayesian formulated rough set model trained using Markov chain Monte Carlo method and 62% obtained from a Bayesian formulated multi-layered perceptron (MLP) model trained using hybrid Monte. The proposed model is able to combine the accuracy of the Bayesian MLP model and the transparency of Bayesian rough set model. 

Keywords: **Neuro-rough model, multi-layered perceptron, Bayesian, HIV modelling** 

## Introduction

The role of machine learning is to be able to make predictions given a set of inputs. However, the other role is to extract rules that govern interrelationships within the data. Machine learning tools such as neural networks are quite good at making predictions 