ORIGINAL RESEARCH article
Front. Tuberc.
Sec. Therapeutic Advances in Tuberculosis and Non-Tuberculous Mycobacterial Disease
Volume 3 - 2025 | doi: 10.3389/ftubr.2025.1659333
Predicting Treatment Adherence in Patients with Drug-Resistant Tuberculosis: Insights from Socioeconomic, Demographic, and Clinical Factors of patients in the rural Eastern Cape
Provisionally accepted
Lindiwe Modest Faye*
Ntandazo Dlatu
Mojisola Clara Hosu
Joshua Iruedo
Prof. Teke tapalata@wsu.ac.za Apalata- Walter Sisulu University, Mthatha, South Africa
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background: Drug-resistant tuberculosis (DR-TB) poses a serious challenge to global health. Patients must follow complex medication regimens over long periods, and any failure to comply with these treatment plans can result in treatment failure, higher mortality rates, and an increased risk of developing additional drug resistance. Setting: The study was conducted in the rural Eastern Cape. Aim: This study aims to identify the key factors influencing treatment adherence among patients with DR-TB. Furthermore, it rigorously evaluates the predictive accuracy of machine learning models in assessing treatment adherence, with a strong focus on socioeconomic, demographic, and clinical factors. Methods: A retrospective analysis was conducted on patients with DR-TB. Data were collected from medical records. Four different models were developed and tested to evaluate their effectiveness in predicting treatment adherence: Random Forest, Logistic regression, Support Vector Machine (SVM), and Gradient Boosting. Results: The Random Forest model achieved an accuracy of 53.3% in predicting treatment adherence. An analysis of feature importance indicated that age, income, education, social history, patient category, and comorbidities were the most significant factors influencing adherence. Patients with higher incomes, higher levels of education, and fewer comorbidities were more likely to follow their treatment plans. Conclusion: Adhering to treatment for DR-TB involves a range of socioeconomic and clinical factors. Income, education level, and pre-existing health conditions significantly influence how well patients follow their prescribed treatment regimens. Understanding these influences is crucial for enhancing treatment outcomes and facilitating patients' journey toward improved health. Contribution: These findings suggest that machine-learning models, especially Random Forest algorithms, can effectively support clinical decision-making by identifying patients at risk of non-adherence to their treatment.
Keywords: Clinical factors, Drug-resistant tuberculosis, Treatment Adherence, Socioeconomic Factors, predictive modelling, random forest model
Received: 03 Jul 2025; Accepted: 28 Aug 2025.
Copyright: © 2025 Faye, Dlatu, Hosu, Iruedo and Apalata. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Lindiwe Modest Faye, Walter Sisulu University, Mthatha, South Africa
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.