Model Based Sparse Feature Extraction for Biomedical Signal Classification
Pages 34-48
Hilda Dhlakama and Siaka Lougue
DOI: https://doi.org/10.6000/1929-6029.2017.06.01.4

Published: 28 February 2017

Abstract: Background: Although the number of deaths has declined since 2007, Tuberculosis (TB) continues to be the number one cause of death in South Africa. To create a country free of TB, there is need for continued research to explore models that will provide the Department of Health with new interventions.

Aim: This study was aimed at identifying the risk factors of active self-reported TB prevalence for South Africa in 2014.

Methods: The Frequentist Logistic Regression (FLR) approach was applied on a sample of 19213 individuals taken from the National Income Dynamics Survey (NIDS) wave data. Bayesian analysis with non-informative priors were used to model Wave 1 to 3 data and elicitation of the obtained posterior density parameters by averaging done to obtain the informative priors used to model wave 4. The wave 4 results obtained under the two estimation approaches were compared as well as the results for non-informative and informative priors.

Results: The findings show that self-reported TB prevalence is higher than the reported 1%, Human Immuno Deficiency Virus (HIV) remains a major threat to TB and Eastern Cape is the province mostly affected by TB with Limpopo recording the least prevalence. Poor living conditions and lower socio-economic conditions continue to be drivers of TB whilst English illiteracy, lack of Secondary/Tertiary education, alcohol consumption, marital status, gender and age groups also influence TB progression to disease. FLR yielded similar results to Bayesian with non-informative priors whilst the results are more precise for informative priors.

Conclusion: This study identified individuals and communities at risk of developing active TB disease in South Africa.

Keywords: Tuberculosis, HIV, South Africa, National Income Dynamics Survey, Bayesian analysis, Frequentist Logistic Regression.

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