Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025.

Journal: BMC medical informatics and decision making
Published Date: Jul 10, 2025

Abstract

BACKGROUND: Adherence with Anti-Retroviral Therapy (ART) reduces viral load, as well as HIV-related morbidity and mortality. Despite the expanded availability of ART, non-adherence remains a series problem, leads increased viral load, a decline CD4 cell count, and the development of drug resistance. HIV care is currently showing promise with the use of machine learning algorithms for early prediction of future non-adherence. However, as to researcher's Knowledge, there was limited research supporting this evidence in the country. Therefore, the primary aim of this study was to predict ART adherence status using machine learning models and to identify the most important predictors of Adherence at Debre Markos comprehensive specialized hospital.

Authors

  • Kelemua Aschale Yeneakal
    Department of Health Informatics, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia. kelemuaaschale64@gmail.com.
  • Gizaw Hailiye Teferi
    Department of Health Informatics, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia.
  • Temesgen T Mihret
    Department of Water Resources and Irrigation Engineering, Asossa University, Asossa, Ethiopia.
  • Abraham Keffale Mengistu
    Department of Health Informatics, College of Medicine Health Science, Debre Markos University, Debre Markos, Ethiopia. abreham_keffale@dmu.edu.et.
  • Sefefe Birhanu Tizie
    Department of Health Informatics, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia.
  • Maru Meseret Tadele
    Department of Health Informatics, College of Health Sciences, Debre Markos University, Debre Markos, Ethiopia.

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