Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

ABSTRACT: Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbidity rates worldwide. Socioeconomic, environmental, and healthcare factors significantly impact TB trends. Therefore, we aimed to predict TB and identify the determinants of the disease using advanced artificial intelligence (AI). This study employed the advanced machine learning (ML) model, XGBoost (eXtreme gradient boosting), combined with XAI (eXplainable artificial intelligence) and spatial analysis to describe global TB incidence and mortality rates across 194 countries from 2000 to 2022. Spatial autocorrelation analysis utilizing Moran's I revealed geographical clusters and significant determinants affecting TB incidence. Treatment success rates and MDR-TB treatment initiation were iden

SUBMITTER: Rahman MS 

PROVIDER: S-EPMC12009319 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature