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.