使用逻辑回归模型预测结核病

K. Ghazvini, S. Mansouri, M. Shakeri, M. Youssefi, M. Derakhshan, M. Keikha
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引用次数: 3

摘要

简介:结核病(TB)是一种慢性细菌性疾病,是继人类免疫缺陷病毒感染之后全球单药感染性疾病中导致死亡的主要原因。逻辑回归是一种具有预测能力的统计分析方法。这种多变量统计方法可用于评估自变量(尽管存在混淆)和因变量之间的相关性。本研究旨在基于logistic回归预测模型的估计来评估影响结核病发病率的因素。方法:横断面研究分为两组,189例结核病患者和189例对照组。比较两组间结核病的影响因素,包括年龄、性别、婚姻状况、获得性免疫缺陷综合征(AIDS)风险、吸烟习惯、哮喘史、器官移植史、体重指数(BMI)、维生素D3水平、糖尿病、血红蛋白及恶性疾病发生率。此外,根据敏感性、特异性、受试者工作特征(ROC)曲线等指标确定logistic回归模型的预测潜力。结果:估计回归模型的灵敏度为78%,特异度为68%,ROC曲线下面积为0.821。在因变量(即TB)中可用的影响因素中,维生素D3和血红蛋白水平以及BMI变量被认为具有显著性。结论:从logistic回归模型预测标准的准确性、预测能力以及ROC曲线下面积(0.821)来看,logistic回归模型适合预测结核病,可为结核病的诊断提供检验准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Tuberculosis Using a Logistic Regression Model
Introduction: Tuberculosis (TB) is a chronic bacterial disease and a leading cause of mortality among single-agent infectious diseases following the human immunodeficiency virus infection across the world. Logistic regression is a method of statistical analysis with predictive capability. This multivariate statistical method could be used to evaluate the correlations between independent variables (albeit confounding) and a dependent variable. The present study aimed to assess the influential factors in the incidence of TB based on the estimations of a logistic regression predictive model.Methods: This cross-sectional study was conducted on two groups consisting of 189 TB patients and 189 controls. The influential factors in TB were compared between the groups, including age, gender, marital status, risk of acquired immunodeficiency syndrome (AIDS), smoking habits, history of asthma, organ transplantation, body mass index (BMI), vitamin D3 level, diabetes, and rate of hemoglobin and malignant diseases. In addition, the predictive potential of the logistic regression model was determined based on various indices, such as sensitivity, specificity, and receiver operating characteristic (ROC) curve. Results: The sensitivity and specificity of the regression model were estimated at 78% and 68%, respectively, and the area under the ROC curve was calculated to be 0.821. Among the available influential factors in the dependent variable (i.e., TB), the variables of vitamin D3 and hemoglobin levels and BMI were considered significant. Conclusion: According to the results, the logistic regression model is appropriate for the prediction of TB considering the accuracy and predictive power of its criteria, as well as the area under the ROC curve (0.821), which could provide the test accuracy for the diagnosis TB.
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