An investigation of multi-label classification techniques for predicting HIV drug resistance in resource-limited settings.
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Date
2014
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Abstract
South Africa has one of the highest HIV infection rates in the world with more than 5.6 million infected
people and consequently has the largest antiretroviral treatment program with more than 1.5 million people
on treatment. The development of drug resistance is a major factor impeding the efficacy of antiretroviral
treatment. While genotype resistance testing (GRT) is the standard method to determine resistance, access
to these tests is limited in resource-limited settings. This research investigates the efficacy of multi-label
machine learning techniques at predicting HIV drug resistance from routine treatment and laboratory data.
Six techniques, namely, binary relevance, HOMER, MLkNN, predictive clustering trees (PCT), RAkEL and
ensemble of classifier chains (ECC) have been tested and evaluated on data from medical records of patients
enrolled in an HIV treatment failure clinic in rural KwaZulu-Natal in South Africa. The performance is
measured using five scalar evaluation measures and receiver operating characteristic (ROC) curves. The
techniques were found to provide useful predictive information in most cases. The PCT and ECC techniques
perform best and have true positive prediction rates of 97% and 98% respectively for specific drugs. The
ECC method also achieved an AUC value of 0:83, which is comparable to the current state of the art. All
models have been validated using 10 fold cross validation and show increased performance when additional
data is added. In order to make use of these techniques in the field, a tool is presented that may, with small
modifications, be integrated into public HIV treatment programs in South Africa and could assist clinicians
to identify patients with a high probability of drug resistance.
Description
M. Sc. University of KwaZulu-Natal, Durban 2014.
Keywords
AIDS vaccines--KwaZulu-Natal., Drug resistance., Drugs--Administration--Computer programmes., HIV antibodies--KwaZulu-Natal., Theses--Computer science.