Medical Journal of the Islamic Republic Of Iran

en Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method Epidemiology Epidemiology Original Research Original Research Background: Tuberculosis is a principal public health issue. Reducing and controlling tuberculosis did not result in the expected success despite implementing effective preventive and therapeutic programs, one of the reasons for which is the delay in definitive diagnosis. Therefore, creating a diagnostic aid system for tuberculosis screening can help in the early diagnosis of this disease. This research aims to use machine learning techniques to identify economic, social, and environmental factors affecting tuberculosis.    Methods: This case-control study included 80 individuals with TB and 172 participants as controls. During January-October 2021, information was collected from thirty-six health centers in Ahvaz, southwest Iran. Five different machine learning approaches were used to identify factors associated with TB, including BMI, sex, age , marital status, education, employment status, size of the family, monthly income, cigarette smoking, hookah smoking, history of chronic illness, history of imprisonment, history of hospital admission, first-class family, second-class family, third-class family, friend, co-worker, neighbor, market, store, hospital, health center, workplace, restaurant, park, mosque, Basij base, Hairdressers and school. The data was analyzed using the  statistical programming R software version 4.1.1.    Results: According to the calculated evaluation criteria, the accuracy level of 5 SVM, RF, LSSVM, KNN, and NB models is 0.99, 0.72, 0.97,0.99, and 0.95, respectively, and except for RF, the other models had the highest accuracy. Among the 39 investigated variables, 16 factors including First-class family (20.83%), friend (17.01%), health center (41.67%), hospital (24.74%), store (18.49%), market (14.32%), workplace (9.46%), history of hospital admission (51.82%), BMI (43.75%), sex (40.36%), age (22.83%), educational status (60.59%), employment status (43.58%), monthly income (63.80%), addiction (44.10%), history of imprisonment (38.19%) were of the highest importance on tuberculosis.    Conclusion: The obtained results demonstrated that machine-learning techniques are effective  in identifying economic, social, and environmental factors associated with tuberculosis. Identifying these different factors plays a significant role in preventing and performing appropriate and timely interventions to control this disease.   Tuberculosis, Classification, Risk factor, Machine Learning 21 27 http://mjiri.iums.ac.ir/browse.php?a_code=A-10-8430-1&slc_lang=en&sid=1 Neda Amoori Epidemiology2012@gmail.com 200319475328460088764 200319475328460088764 No Infectious and Tropical Diseases Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Bahman Cheraghian cheraghian2000@yahoo.com 200319475328460088765 200319475328460088765 No Department of Biostatistics and Epidemiology, School of Public Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Payam Amini payam.amini87@gmail.com 200319475328460088766 200319475328460088766 No School of Medicine, Keele University, Staffordshire, UK Seyed Mohammad Alavi alavi_sm@ajums.ac.ir 200319475328460088767 200319475328460088767 Yes Infectious and Tropical Diseases Research Center, Health Research Institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran