Modelling of a sensitive type of mycobacterium tuberculosis using regression model analysis for non-invasive technique detection / Raja Umi Kalsom Raja Mohd Radzi

Raja Mohd Radzi, Raja Umi Kalsom (2017) Modelling of a sensitive type of mycobacterium tuberculosis using regression model analysis for non-invasive technique detection / Raja Umi Kalsom Raja Mohd Radzi. Masters thesis, Universiti Teknologi MARA.

Abstract

Tuberculosis disease has become an infectious disease and one of the leading causes of mortality and morbidity in the world. The conventional diagnosis and method used to detect the Mycobacterium Tuberculosis is time consuming, invasive, tiring, labor intensive and requires the microbiologist expertise to confirm the accuracy of the results. There is no electrical instrument to detect Tuberculosis automatically, no electronic circuit model for evaluating the instrument and no research has been carried out to model the sensitive type of Mycobacterium Tuberculosis. This project concerns with the development of an electronic circuit that models the sensitive type of Mycobacterium Tuberculosis. The aims of the research are to design and simulate circuit models that demonstrate the sensitive type of Mycobacterium Tuberculosis and to evaluate the performance of the model. In the model development, the collection rate of Mycobacterium Tuberculosis obtained from the previous studies was first converted to gain. Regression Model Analysis was carried out, followed by the design of the passive low pass filter, RC, LC and RLC circuits, circuits simulation and fabrication process. The best model of the sensitive type of Mycobacterium Tuberculosis is the second order of LC simulation circuit since it provides less than 10% discrepancy. From the simulation results, it was found that the logarithmic regression model is the best equation that demonstrates the sensitive type of Mycobacterium Tuberculosis.

Metadata

Item Type: Thesis (Masters)
Creators:
Creators
Email / ID Num.
Raja Mohd Radzi, Raja Umi Kalsom
2010288526
Contributors:
Contribution
Name
Email / ID Num.
Thesis advisor
Mansor, Wahidah (Prof. Madya Datin Dr.)
UNSPECIFIED
Subjects: R Medicine > RC Internal Medicine > Neuroscience. Biological psychiatry. Neuropsychiatry
R Medicine > RC Internal Medicine > Tuberculosis
Divisions: Universiti Teknologi MARA, Shah Alam > Faculty of Electrical Engineering
Programme: Master of Science - EE780
Keywords: mycobacterium, tuberculosis, disease
Date: February 2017
URI: https://ir.uitm.edu.my/id/eprint/39270
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