Predictive machine learning models for anticipating loss to follow-up in tuberculosis patients throughout anti-TB treatment journey.

ABSTRACT: Loss to follow-up (LTFU) in tuberculosis (TB) management increases morbidity and mortality, challenging effective control strategies. This study aims to develop and evaluate machine learning models to predict loss to follow-up in TB patients, improving treatment adherence and outcomes. Retrospective data encompassing tuberculosis patients who underwent treatment or registration at the National Center for Clinical Medical Research on Infectious Diseases from January 2017 to December 2021 were compiled. Employing machine learning techniques, namely SVM, RF, XGBoost, and logistic regression, the study aimed to prognosticate LTFU. A comprehensive cohort of 24,265 tuberculosis patients underwent scrutiny, revealing a LTFU prevalence of 12.51% (n = 3036). Education level, history of hospitalizat

SUBMITTER: Chen J 

PROVIDER: S-EPMC11494039 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature