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Explainable AI–Driven Comparative Analysis of Machine Learning Models for Predicting HIV Viral Nonsuppression in Ugandan Patients: Retrospective Cross-Sectional Study
HIV viral suppression is essential for improving health outcomes and reducing transmission rates among people living with HIV. In Uganda, where HIV/AIDS is a major public health concern, machine learning (ML) models can predict viral suppression effectively. However, the limited use of explainable artificial intelligence (XAI) methods affects model transparency and clinical utility.
Medigy Insights
Using explainable AI, machine learning models like XGBoost can accurately predict which patients with HIV are likely to have viral nonsuppression (higher viral load), highlighting key risk factors such as recent medication adherence, age, residence, and treatment duration.
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