Computational intelligence methods for risk assessment of HIV

Taryn N. Tim, T. M. Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Demographic and medical history information obtained from annual South African antenatal surveys is used to estimate the risk of acquiring HIV. Biomedical individualism refers to the factors that place an individual at risk of acquiring an infectious disease, which affects the risk profile of that individual. The design of the estimation system consists of two stages, the first of which is a neural network trained to perform binary classification, using supervised learning with the survey data. The survey information containing discrete variables such as age, gravidity and parity, as well as the quantitative variables race and location, make up the input to the neural network, and the HIV status as the output. A multilayer perceptron with a logistic function is trained with a cross entropy error function, which allows for a probabilistic interpretation of the output. Predictive and classification performance, sensitivity and specificity are measured, and the Receiver Operating Characteristic is derived. This curve illustrates the ability of the classifier to produce a binary output based on varying thresholds. In the second part of the system, the trained neural network produces the inferred risk probability, using Bayesian classification methods to estimate the class conditional densities. The predicted posterior probability is adjusted using Bayes Theorem to account for differing prior probabilities of the training and testing data. One of the difficulties encountered in the survey data is missing data, and this presents a significant problem with neural networks, as they are unable to make predictions using partially complete inputs. An auto-associative neural network is trained on complete datasets, and when presented with partial data, global optimization methods are used to approximate the missing entries. The effect of the imputed data on the network prediction is investigated.

Original languageEnglish
Title of host publicationIFMBE Proceedings
EditorsSun I. Kim, Tae Suk Suh
PublisherSpringer Verlag
Pages3717-3721
Number of pages5
Edition1
ISBN (Print)9783540368397
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event10th World Congress on Medical Physics and Biomedical Engineering, WC 2006 - Seoul, Korea, Republic of
Duration: 27 Aug 20061 Sept 2006

Publication series

NameIFMBE Proceedings
Number1
Volume14
ISSN (Print)1680-0737
ISSN (Electronic)1433-9277

Conference

Conference10th World Congress on Medical Physics and Biomedical Engineering, WC 2006
Country/TerritoryKorea, Republic of
CitySeoul
Period27/08/061/09/06

Keywords

  • Classification
  • Neural networks
  • Risk assessment

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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