Author(s)

Kexin Guo ¹, Xiaoran Xu ¹, Qinge Zhan ¹, Li Guo ², Feng Feng ¹

Affiliation(s)

¹ School of Medicine, Shihezi University, Shihezi, 832003, Xinjiang, China
² Xinjiang Production and Construction Corps Second Division 30th Regiment Hospital, Tiemenguan, 841000, Xinjiang, China

Corresponding Author

Feng Feng

ABSTRACT

Tuberculosis (TB), long established as a key factor in morbidity and mortality throughout the world. TB not only jeopardizes the health of individuals, but also imposes a heavy burden on society and the economy. Therefore, there is an urgent need for prevention and treatment studies to address this health problem. The aim of this study was to evaluate the predisposing factors of tuberculosis and develop predictive models to identify high-risk groups. The incidence of tuberculosis in 2022 was 133/100,000, which is an increase of 3.9% over the period 2020-2022, against the target of "ending the tuberculosis epidemic". The study collected data from 2032 patients and analyzed key factors such as age, history of tobacco use, gender, alcohol consumption, malnutrition and diabetes through logistic regression, decision tree and random forest models. The results showed that history of tobacco use, history of alcohol consumption, malnutrition and diabetes mellitus were the main causative factors, while age had a weaker relationship. Among the models, logistic regression (91.97% correct for logit and 88.68% for probit), decision tree (89.77% correct), and random forest (97.87% correct) predicted well, with random forest being the best. This research contributes to optimizing the detection and management processes for high-risk populations through enhanced preventive strategies.

KEYWORDS

Tuberculosis, Influencing Factors, Logistic Regression, Decision Tree Model

CITE THIS PAPER

Kexin Guo, Xiaoran Xu, Qinge Zhan, Li Guo, Feng Feng, Study on Influencing Factors of Tuberculosis Based on Logistic Regression and Decision Tree Model. Social Medicine and Health Management (2025) Vol. 6: 115-122. DOI: http://dx.doi.org/10.23977/socmhm.2025.060115.

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