Visual analytics of tuberculosis detection rat Performance
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Date
2021
Authors
Jonathan, Joan
Sanga, Camilius
Mwita, Magesa
Mgode, Georgies
Journal Title
Journal ISSN
Volume Title
Publisher
Online Journal of Public Health Informatics
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.
Description
Journal Article
Keywords
Data mining in healthcare, African giant pouched rats, Classification Technique in Tuberculosis diagnosis