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

2014

  • Authors:
    Pascal Brandt , Deshen Moodley , Anban Pillay , Chris Seebregts , de Oliveira, T.

    Publication date:
    2014

    Institution:

    Output type:
    Conference proceedings

    Springer Berlin Heidelberg

    Abstract:

    The development of drug resistance is a major factor imped- ing 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 set- tings. 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 tech- niques, including binary relevance, HOMER, MLkNN, predictive clus- tering trees (PCT), RAkEL and ensemble of classifier chains were tested on a dataset of 252 medical records of patients enrolled in an HIV treat- ment 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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