ArticleScientific reports2025
Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Explainable prediction of MDR/RR-TB in tuberculosis-diabetes mellitus multimorbidity: a machine learning model developed and validated in a dual-center study.BMC infectious diseases · 2026Article
- Observational
- AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda.Tropical medicine and infectious disease · 2026Article
- Trends and spatial distribution of pulmonary tuberculosis in China: a surveillance study.Frontiers in public health · 2026Article
- Artificial intelligence and tobacco use: A bibliometric analysis 1997-2026.Tobacco induced diseases · 2026Article
- Analysis of High-Risk Factors for Tuberculosis Retreatment Based on Machine Learning and Latent Class Analysis.Infection and drug resistance · 2026Article
- Leveraging explainable artificial intelligence and spatial analysis for communicable diseases in Asia (2000-2022) based on health, climate, and socioeconomic factors.International journal of health geographics · 2025Article
- Reflections on explainable artificial intelligence for predicting dengue outbreaks in Bangladesh.Global epidemiology · 2025Article
- Explainable machine learning for predicting clinical outcomes in HIV/TB co-infection: a comparative retrospective study.BMC infectious diseases · 2025Article
- Unraveling global malaria incidence and mortality using machine learning and artificial intelligence-driven spatial analysis.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
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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 identified as pivotal determinants of TB incidence. The correlation study revealed a substantial positive relationship between TB incidence in HIV-positive patients and overall TB incidence (r = 0.83). Confirmed cases of MDR-TB had the most significant impact on TB incidence (SHAP = 0.874). Additionally, air pollution had a notable impact on TB incidence (SHAP = 1.36). The XGBoost model demonstrated the best predictive performance for TB incidence and mortality, exhibiting the lowest RMSE (0.88), the highest R
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