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International Symposium on Foundations of Health Informatics Engineering and Systems

FHIES 2013: Foundations of Health Information Engineering and Systems pp 236–253Cite as

An Investigation of Classification Algorithms for Predicting HIV Drug Resistance without Genotype Resistance Testing

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8315))

Abstract

The development of drug resistance is a major factor impeding the efficacy of antiretroviral treatment of South Africa’s HIV infected population. While genotype resistance testing is the standard method to determine resistance, access to these tests is limited in low-resource settings. In this paper we investigate machine learning techniques for drug resistance prediction from routine treatment and laboratory data to help clinicians select patients for confirmatory genotype testing. The techniques, including binary relevance, HOMER, MLkNN, predictive clustering trees (PCT), RAkEL and ensemble of classifier chains were tested on a dataset of 252 medical records of patients enrolled in an HIV treatment failure clinic in rural KwaZulu-Natal in South Africa. The PCT method performed best with a discriminant power of 1.56 for two drugs, above 1.0 for three others and a mean true positive rate of 0.68. These methods show potential for application where access to genotyping is limited.

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Author information

Authors and Affiliations

  1. Centre for Artificial Intelligence Research and Health Architecture Laboratory, University of KwaZulu-Natal, Durban/CSIR Meraka, Pretoria, South Africa

    Pascal Brandt, Deshendran Moodley, Anban W. Pillay & Christopher J. Seebregts

  2. Jembi Health Systems, Cape Town and Durban, South Africa

    Pascal Brandt & Christopher J. Seebregts

  3. Africa Centre for Health and Population Studies, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa

    Tulio de Oliveira

Authors
  1. Pascal Brandt
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  2. Deshendran Moodley
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  3. Anban W. Pillay
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  4. Christopher J. Seebregts
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  5. Tulio de Oliveira
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Oxford University, Wolfson Building, Parks Road, OX1 3QD, Oxford, UK

    Jeremy Gibbons

  2. Department of Mathematics, St. Francis Xavier University, P.O. Box 5000, B2G 2W5, Antigonish, NS, Canada

    Wendy MacCaull

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Brandt, P., Moodley, D., Pillay, A.W., Seebregts, C.J., de Oliveira, T. (2014). An Investigation of Classification Algorithms for Predicting HIV Drug Resistance without Genotype Resistance Testing. In: Gibbons, J., MacCaull, W. (eds) Foundations of Health Information Engineering and Systems. FHIES 2013. Lecture Notes in Computer Science, vol 8315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53956-5_16

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  • DOI: https://doi.org/10.1007/978-3-642-53956-5_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53955-8

  • Online ISBN: 978-3-642-53956-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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