Abstract:
Abstract
Introduction: Treatment with effective antiretroviral therapy (ART) reduces HIVrelated
morbidity
and
mortality.
Despite
the
widespread
use
of
antiretroviral
treatment
(ART),
34%
of
HIV-positive
individuals
worldwide
and
48%
of
those
in
Africa
develop
virological
failure. Thus, this study used different machine learning classification
algorithms to predict the features that cause virological failure in HIV-positive patients.
Objectives: This study aimed to predict virological failure among HIV patients on
antiretroviral therapy at the University of Gondar Comprehensive and Specialized
Hospital, Ethiopia, 2022: using machine learning algorithms.
Method: An institution-based secondary data was used to conduct patients who were
on antiretroviral therapy (ART) at the University of Gondar Comprehensive and
Specialized Hospital. Patients' data from electronic databases were extracted and
imported into Python version three software for data preprocessing and analysis.
Then, seven supervised classification machine-learning algorithms for model
development were trained. Eventually, the performance of the predictive models was
evaluated using accuracy, sensitivity, specificity, precision, f1-score, and AUC.
Association rule mining was used to generate the best rule for the association between
independent features and the target feature.
Result: Out of 5264 study participants, 1893 (35.06%) males and 3371 (64.04%)
females were included. The random forest classifier (sensitivity = 1.00, precision =
0.987, f1-score = 0.993, AUC = 0.9989) outperformed in predicting virological failure
among all selected classifiers. Random forest feature importance and association
rules identified the top eight predictors of virological failure based on the importance
ranking, and the CD-4 count was recognized as the most important predictor feature.
Conclusion: The random forest classifier outperformed in predicting and identifying
the relevant predictors of virological failure. Male, younger age, longer duration on
ART, not taking CPT, not taking TPT, secondary educational status, TDF-3TC-EFV,
and low CD4 counts were relevant features for predicting virological failure.
Keywords: HIV/AIDS, Virological Failure, Machine learning, Antiretroviral Treatment,
Ethiopia.
