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This paper is in the following e-collection/theme issue:

Research Methodology - AI (153) Clinical Information and Decision Making (3569) HIV/AIDS/STI Prevention and Care (1274) Health Services in Resource-Poor Settings and LMICs (666) Machine Learning (3071) Artificial Intelligence (4575) AI Applications in Public Health (94)

Published on in Vol 5 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68196, first published .
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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

Explainable AI–Driven Comparative Analysis of Machine Learning Models for Predicting HIV Viral Nonsuppression in Ugandan Patients: Retrospective Cross-Sectional Study

Authors of this article:

Francis Ngema1 Author Orcid Image ;   Albert Whata2 Author Orcid Image ;   Micheal O Olusanya3 Author Orcid Image ;   Siyabonga Mhlongo4 Author Orcid Image
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Journals

  1. Namaganda M, Gennatas S, Merson L, Garcia E, Edinburgh T, Nabende J, Kateete D, Batte C, Wayengera M, Jjingo D, Kigozi E, Mboowa G. Predicting HIV viral non-suppression in Uganda: development and validation of machine learning and risk stratification models using routine EMR data. Frontiers in Artificial Intelligence 2026;9 View