Mycobacterium tuberculosis modelling using regression analysis

Abstract

The conventional diagnosis method used to detect the Mycobacterium tuberculosis is invasive which requires the blood is taken from the patients or tissue is removed from the patient's organ. The non-invasive detection tool is not available and there is no electronic-based model to examine the detection mechanism and predict its performance. This paper describes the modelling of the sensitive type of the Mycobacterium tuberculosis using regression analysis. The collection rate of Mycobacterium tuberculosis obtained from the previous studies served as the basis for the model creation and optimal model selection. Two types of the LC circuits, the first order, and the second order were investigated in this work. Regression analysis and one-way analysis of variance were carried out to confirm the optimum model. The second order LC circuit provides the least error and variation and could mimic the sensitive type of Mycobacterium tuberculosis.