Neuro-rough models for modelling HIV

Tshilidzi Marwala, Bodie Crossingham

Research output: Contribution to journalConference articlepeer-review

15 Citations (Scopus)


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.

Original languageEnglish
Article number4811770
Pages (from-to)3089-3095
Number of pages7
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - 2008
Externally publishedYes
Event2008 IEEE International Conference on Systems, Man and Cybernetics, SMC 2008 - Singapore, Singapore
Duration: 12 Oct 200815 Oct 2008


  • Bayesian MLP
  • HIV
  • Nerual networks
  • Rough sets

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Human-Computer Interaction


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