Clinical value analysis of influencing factors in predicting PTB treatment outcome.

<p>(A): The ROC curve; (B): The DCA curve; (C): The results of the ranking of factors.</p>

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Bibliographic Details
Main Author:	Xiao-yan Xu (729227) (author)
Other Authors:	Zheng-yuan Zhou (20571317) (author), Li-qiang Gong (20571320) (author), Li-qiang Xu (20571323) (author), Xiao-kang Jiao (20571326) (author), Bian Yin (13025541) (author), Tian-hong Jiang (20571329) (author)
Published:	2025
Subjects:	Cell Biology Biotechnology Cancer Mental Health Infectious Diseases Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified univariate logistic regression tb strain types suzhou statistical yearbook receiver operating characteristic multivariate logistic regression annual incidence rate 081 per 100 china information system hot spot areas cured ptb patients 3943 ptb cases high cluster areas high aggregation areas 2022 </ p decision curve analysis pulmonary tuberculosis cure southern areas pulmonary tuberculosis information came hot spots new ptb xlink "> xgboost analysis township scale towns came study aimed shajiabang town risk factor predictive efficacy mycobacterium tuberculosis landscan platform key metrics jiangsu province influencing factors independent predictors feature importance drug sensitivity diagnosis delay demographic data clinical benefit cisdcp ). accurate methods
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