Electronic-based model of the sensitive type of mycobacterium tuberculosis

R. U.K. Raja Mohd Radzi, Wahidah Mansor, Juliana Johari

Research output: Contribution to journalConference article

Abstract

Early diagnosis of Tuberculosis disease is important to prevent complicated issues from arising. The conventional techniques used to diagnose the Mycobacterium Tuberculosis have limitations such as time-consuming, invasive, tiring, labour intensive and microbiologist expert dependence. There is no electrical instrument to detect Tuberculosis automatically and no electronic circuit model for evaluating the device. This paper describes the development of an electronic circuit model of the sensitive type of Mycobacterium Tuberculosis. The model was developed by first converting the real data to gain, deriving the model equations using Regression model analysis and performing one-way ANOVA to confirm the results. Three types of model were investigated; the first order, second order and third order LC passive low pass filter circuits. It was found that the second order of LC circuit is the best model of the sensitive type of Mycobacterium Tuberculosis as it provides less than 10% discrepancy. The simulation results show that the logarithmic regression model is the best equation that demonstrates the sensitive type of Mycobacterium Tuberculosis.

LanguageEnglish
Pages597-600
Number of pages4
JournalIFMBE Proceedings
Volume68
Issue number1
DOIs
Publication statusPublished - Jan 1 2019
EventWorld Congress on Medical Physics and Biomedical Engineering, WC 2018 - Prague, Czech Republic
Duration: Jun 3 2018Jun 8 2018

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Networks (circuits)
Passive filters
Low pass filters
Analysis of variance (ANOVA)
Personnel

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Biomedical Engineering

Cite this

Electronic-based model of the sensitive type of mycobacterium tuberculosis. / Raja Mohd Radzi, R. U.K.; Mansor, Wahidah; Johari, Juliana.

In: IFMBE Proceedings, Vol. 68, No. 1, 01.01.2019, p. 597-600.

Research output: Contribution to journalConference article

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