Identification of Risk Factors Associated with Tuberculosis in Southwest Iran - A Machine Learning Method

Aug 2, 2026

Tuberculosis remains a major public health challenge, and delays in definitive diagnosis limit the success of prevention and treatment programs. This case-control study develops a machine-learning diagnostic aid to detect economic, social, and environmental factors linked to tuberculosis. Data from 80 TB patients and 172 controls were collected at 36 health centers in Ahvaz during January–October 2021. Five models were evaluated using R, achieving high accuracy for most approaches.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages7

Reading Time18 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusion

Keywords

Introduction

Public health significance of TB

Reported risk factors and motivation

Original Article [http://mjiri.iums.ac.ir](http://mjiri.iums.ac.ir) Medical Journal of the Islamic Republic of Iran (MJIRI) MedJ Islam Repub Iran. 2024 (17 Jan);38.5. [https://doi.org/10.47176/mjiri.38.5](https://doi.org/10.47176/mjiri.38.5) Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method Neda Amoori1, Bahman Cheraghian2, Payam Amini3, Seyed Mohammad Alavi1 * Received: 12 Aug 2023 Published: 17 Jan 2024 Abstract 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. Keywords: Tuberculosis, Classification, Risk factor, Machine Learning Conflicts of Interest: None declared Funding: The Vice-Chancellor for Research atAhvaz Jundishapur University of Medical Sciences provided financial support. *This work has been published under CC BY-NC-SA 1.0 license. Copyright© Iran University of Medical Sciences Cite this article as: Amoori N, Cheraghian B, Amini P, Alavi SM. Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method. MedJ Islam Repub Iran. 2024 (17 Jan);38:5. [https://doi.org/10.47176/mjiri.38.5](https://doi.org/10.47176/mjiri.38.5) Introduction Tuberculosis(TB) is a significant public health concern and is currently recognized as the most fatal treatable infectious disease in the world (1) . As per the estimates of the World Health Organization (WHO), in 2020, 9.9 million individuals were infected with tuberculosis and this disease caused 1.3 million deaths (2). Worldwide, Eastern Mediterranean region, Iran,and Khuzestan province had an inci- | Corresponding author: Dr Seyed Mohammad Alavi, [alavi_sm@ajums.ac.i

FAQs

What is the main goal of this study?

To use machine-learning techniques to identify economic, social, and environmental factors associated with tuberculosis, supporting earlier diagnosis and screening.

How was the study conducted?

A case-control design included 80 individuals with TB and 172 controls. Data were collected from 36 health centers in Ahvaz, southwest Iran, during January–October 2021, and analyzed using R.

Which machine learning models were evaluated and what performance was achieved?

Five models—SVM, RF, LSSVM, KNN, and NB—were tested. Reported accuracy levels were 0.99, 0.72, 0.97, 0.99, and 0.95 respectively, with the highest accuracy except for RF.

Aug 2, 2026

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