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

Research Methodology - AI (172) Clinical Information and Decision Making (3667) HIV/AIDS/STI Prevention and Care (1282) Health Services in Resource-Poor Settings and LMICs (678) Machine Learning (3134) Artificial Intelligence (4712) AI Applications in Public Health (99)

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
Article Authors Cited by (2) Tweetations Metrics

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
  2. Das B, Uslu H, Bostancioglu A, Goktas B, Santur Y, Yilmaz S, Türkoğlu İ. A Comparative Evaluation Framework Integrating Machine Learning and Deep Learning Models with ADME-Based Pharmacokinetic Assessment for HIV-Related Compounds. Pharmaceuticals 2026;19(8):1267 View