A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district

ABSTRACT: Despite the availability and implementation of well-known efficacious interventions for tuberculosis treatment by the Ministry of Health, Uganda (MoH), treatment non-adherence persists. Moreover, identifying a specific tuberculosis patient at risk of treatment non-adherence is still a challenge. Thus, this retrospective study, based on a record review of 838 tuberculosis patients enrolled in six health facilities, presents, and discusses a machine learning approach to explore the individual risk factors predictive of tuberculosis treatment non-adherence in the Mukono district, Uganda. Five classification machine learning algorithms, logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and AdaBoost were trained, and evaluated by comp

SUBMITTER: Gichuhi H 

PROVIDER: S-EPMC10317231 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature