Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning.

ABSTRACT:

Background

Tuberculosis (TB) continues to pose a significant threat to global public health. Enhancing patient prognosis is essential for alleviating the disease burden.

Objective

This study aims to evaluate TB prognosis by incorporating treatment discontinuation into the assessment framework, expanding beyond mortality and drug resistance.

Methods

Seven feature selection methods and twelve machine learning algorithms were utilized to analyze admission test data from TB patients, identifying predictive features and building prognostic models. SHapley Additive exPlanations (SHAP) were applied to evaluate feature importance in top-performing models.

Results

Analysis of 1,086 TB cases showed that a K-Nearest Neighbor classifier with Mutual Information feature sele

SUBMITTER: Wang Z 

PROVIDER: S-EPMC11834319 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature