Multi-horizon predictive modeling of HIV treatment adherence using clinical and social determinants in Uganda

Abstract

Treatment interruption among people living with HIV (PLHIV) remains a critical challenge to achieving sustained viral suppression and optimal treatment outcomes, particularly in sub- Saharan Africa. In Uganda, despite significant progress in scaling up antiretroviral therapy (ART), a substantial proportion of patients experience interruptions in treatment, contributing to viral rebound, increased transmission risk, and drug resistance. The adoption of the Multi- Month Dispensing (MMD) model, while improving access and convenience, reduces routine patient-provider contact and may delay early identification of patients at risk of disengagement. This study aimed to develop and evaluate a machine learning-based predictive framework for identifying patients at risk of treatment interruption under Uganda’s MMD system. A quantitative research design was employed using retrospective data extracted from Electronic Medical Records (EMRs) of 8,788 patients receiving ART. Data preprocessing involved cleaning, feature engineering, and handling missing values, followed by the development of machine learning models using a structured pipeline. The Random Forest algorithm was selected as the primary model due to its ability to capture complex nonlinear relationships and its robustness in handling imbalanced clinical datasets. The models were trained and evaluated across three temporal prediction windows (30, 60, and 90 days) using performance metrics including precision, recall, F1-score, and ROC-AUC. Particular emphasis was placed on precision to ensure reliable identification of high-risk patients while minimizing false positive classifications. The findings demonstrate that integrating clinical, demographic, and behavioral factors improves the predictive ability of machine learning models in identifying treatment interruption risk. Key predictors included viral load status, CD4 count, duration on ART, and selected socio-demographic characteristics. The developed framework enables the generation of individualized risk scores and prediction of likely interruption periods, supporting proactive patient management. This study contributes to the growing body of evidence on the application of machine learning in HIV care and highlights the importance of incorporating multidimensional determinants in predictive modelling. The proposed model provides a practical tool for early risk identification and targeted intervention, with potential to enhance patient retention and improve treatment outcomes within Uganda’s HIV care system.