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. 2021 Sep 8;13(2):e12.
doi: 10.5210/ojphi.v13i2.11465. eCollection 2021.

Visual Analytics of Tuberculosis Detection Rat Performance

Joan Jonathan  1 , Camilius Sanga  2 , Magesa Mwita  1 , Georgies Mgode  3   4
Affiliations

Visual Analytics of Tuberculosis Detection Rat Performance

Joan Jonathan et al. Online J Public Health Inform. .

Abstract

The diagnosis of tuberculosis (TB) disease remains a global challenge, and the need for innovative diagnostic approaches is inevitable. Trained African giant pouched rats are the scent TB detection technology for operational research. The adoption of this technology is beneficial to countries with a high TB burden due to its cost-effectiveness and speed than microscopy. However, rats with some factors perform better. Thus, more insights on factors that may affect performance is important to increase rats' TB detection performance. This paper intends to provide understanding on the factors that influence rats TB detection performance using visual analytics approach. Visual analytics provide insight of data through the combination of computational predictive models and interactive visualizations. Three algorithms such as Decision tree, Random Forest and Naive Bayes were used to predict the factors that influence rats TB detection performance. Hence, our study found that age is the most significant factor, and rats of ages between 3.1 to 6 years portrayed potentiality. The algorithms were validated using the same test data to check their prediction accuracy. The accuracy check showed that the random forest outperforms with an accuracy of 78.82% than the two. However, their accuracies difference is small. The study findings may help rats TB trainers, researchers in rats TB and Information systems, and decision makers to improve detection performance. This study recommends further research that incorporates gender factors and a large sample size.

Keywords: African giant pouched rats; Classification Technique in Tuberculosis diagnosis; Data mining in healthcare.

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Conflict of interest statement

Conflicts of interest On behalf of all authors, the corresponding author states that there is no conflict of interest.

Figures

Figure 1
Figure 1
Decision tree with rats factors that influence TB detection performance
Figure 2
Figure 2
Variable importance generated by Random Forest algorithm
See this image and copyright information in PMC

References

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