Machine Learning-Based Classification of HIV Viral Load Suppression in Low-Resource Settings

Abstract Background Effective viral load (VL) monitoring is crucial in the management of HIV care, but is difficult in resource-constrained settings due to limited access to laboratory examinations. Machine learning (ML) has a promising approach to viral load suppression (VLS) prediction using normal clinical information. This study aimed to develop and interpret an ML model for VLS classification among an Ethiopian cohort. Methods A retrospective analysis was undertaken with electronic medical records of 4,152 patients on antiretroviral therapy (ART) in the University of Gondar Comprehensive Specialized Hospital. Eight ML algorithms, namely Logistic Regression, Random Forest, and Gradient Boosting, were trained and optimized to classify a binary VLS outcome. Model performance was assessed based on accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). The best-performing model was interpreted with SHapley Additive exPlanations (SHAP) to identify the significant predictors and their sign of impact. Results The best-performing Gradient Boosting model performed the best with 76% accuracy, 0.74 F1-score, and 0.79 AUC-ROC. Baseline CD4 Category and Duration on ART in Months were identified as the most impactful predictors through feature importance evaluation. SHAP analysis supported that longer ART duration and larger baseline CD4 count were associated with increased odds of VLS, and that higher WHO clinical stage and male sex were associated with unsuppressed VL. The model's decision-making was further depicted for individual patients by waterfall plots, which enhanced clinical interpretability. Conclusion This work demonstrates that one can have an interpretable Gradient Boosting model to properly predict viral load suppression in a low-resource setting. The predictions of the model are made from clinically reasonable factors, linking algorithmic performance to corresponding clinical insight. The tool can potentially assist healthcare workers in identifying patients at risk of treatment failure, enabling the implementation of early interventions and optimizing HIV care management in settings where routine VL testing is not feasible.