伊朗西南部结核病相关风险因素的识别:机器学习方法

Q2 Medicine
Medical Journal of the Islamic Republic of Iran Pub Date : 2024-01-17 eCollection Date: 2024-01-01 DOI:10.47176/mjiri.38.5
Neda Amoori, Bahman Cheraghian, Payam Amini, Seyed Mohammad Alavi
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引用次数: 0

摘要

背景:结核病是一个主要的公共卫生问题。尽管实施了有效的预防和治疗计划,但减少和控制结核病并没有取得预期的成功,其中一个原因是明确诊断的延误。因此,建立结核病筛查诊断辅助系统有助于该疾病的早期诊断。本研究旨在利用机器学习技术识别影响结核病的经济、社会和环境因素:这项病例对照研究包括 80 名肺结核患者和 172 名对照者。2021 年 1 月至 10 月期间,研究人员从伊朗西南部阿瓦士的 36 个医疗中心收集了相关信息。研究采用了五种不同的机器学习方法来识别与肺结核相关的因素,包括体重指数、性别、年龄、婚姻状况、教育程度、就业状况、家庭人口数、月收入、吸烟、吸水烟、慢性病史、入狱史、入院史、一等家庭、二等家庭、三等家庭、朋友、同事、邻居、市场、商店、医院、保健中心、工作场所、餐厅、公园、清真寺、巴斯基基地、理发店和学校。数据使用 4.1.1 版 R 统计编程软件进行分析:根据计算得出的评价标准,5 个 SVM、RF、LSSVM、KNN 和 NB 模型的准确度分别为 0.99、0.72、0.97、0.99 和 0.95,除 RF 外,其他模型的准确度最高。在调查的 39 个变量中,一级家庭(20.83%)、朋友(17.01%)、卫生院(41.67%)、医院(24.74%)、商店(18.49%)、市场(14.32%)、工作单位(9.46%)、入院史(51.82%)、体重指数(43.75%)、性别(40.36%)、年龄(22.83%)、教育状况(60.59%)、就业状况(43.58%)、月收入(63.80%)、成瘾(44.10%)、入狱史(38.19%)对肺结核的影响最大:研究结果表明,机器学习技术能有效识别与肺结核有关的经济、社会和环境因素。识别这些不同的因素对于预防和及时采取适当的干预措施控制该疾病具有重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method.

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.

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CiteScore
2.40
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0.00%
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