Machine learning model to predict the adherence of tuberculosis patients experiencing increased levels of liver enzymes in Indonesia.

ABSTRACT: Indonesia is still the second-highest tuberculosis burden country in the world. The antituberculosis adverse drug reaction and adherence may influence the success of treatment. The objective of this study is to define the model for predicting the adherence in tuberculosis patients, based on the increased level of liver enzymes. The longitudinal study using adult tuberculosis patients treated with the first line of antituberculosis was conducted prospectively. The pregnant women and patients with complications such as gout, diabetes mellitus, liver disorder and HIV were excluded. We measured the total bilirubin, aspartate aminotransferase (AST), and alanine aminotransferase (ALT) and adherence over the 2nd, 4th, and 6th months of the treatment. We used the ORANGE Data mining as the machine

SUBMITTER: Perwitasari DA 

PROVIDER: S-EPMC11760577 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature