Predicting Virological Failure among HIV Patients on Antiretroviral Therapy in the University of Gondar Comprehensive and Specialized Hospital, in Amhara Region, Northwest Ethiopia 2022: Using Machine Learning Algorithms

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.