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. 2024 Apr 16:16:e50771.
doi: 10.2196/50771.

Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats

Joan Jonathan #  1 , Alcardo Alex Barakabitze #  1 , Cynthia D Fast #  2   3   4 , Christophe Cox #  2
Affiliations

Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats

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

Abstract

Background: Technological advancement has led to the growth and rapid increase of tuberculosis (TB) medical data generated from different health care areas, including diagnosis. Prioritizing better adoption and acceptance of innovative diagnostic technology to reduce the spread of TB significantly benefits developing countries. Trained TB-detection rats are used in Tanzania and Ethiopia for operational research to complement other TB diagnostic tools. This technology has increased new TB case detection owing to its speed, cost-effectiveness, and sensitivity.

Objective: During the TB detection process, rats produce vast amounts of data, providing an opportunity to identify interesting patterns that influence TB detection performance. This study aimed to develop models that predict if the rat will hit (indicate the presence of TB within) the sample or not using machine learning (ML) techniques. The goal was to improve the diagnostic accuracy and performance of TB detection involving rats.

Methods: APOPO (Anti-Persoonsmijnen Ontmijnende Product Ontwikkeling) Center in Morogoro provided data for this study from 2012 to 2019, and 366,441 observations were used to build predictive models using ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor, by incorporating a variety of variables, such as the diagnostic results from partner health clinics using methods endorsed by the World Health Organization (WHO).

Results: The support vector machine technique yielded the highest accuracy of 83.39% for prediction compared to other ML techniques used. Furthermore, this study found that the inclusion of variables related to whether the sample contained TB or not increased the performance accuracy of the predictive model.

Conclusions: The inclusion of variables related to the diagnostic results of TB samples may improve the detection performance of the trained rats. The study results may be of importance to TB-detection rat trainers and TB decision-makers as the results may prompt them to take action to maintain the usefulness of the technology and increase the TB detection performance of trained rats.

Keywords: African giant pouched rat; diagnosis; health care; machine learning; tuberculosis.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Tuberculosis (TB) testing and detection using trained rats. The rats test and detect TB-negative and TB-positive samples.
Figure 2
Figure 2
Relationship among the status of tuberculosis (TB), hit, and the performance of the rat. Hit refers to whether or not (true or false) the rat provided an indication.
Figure 3
Figure 3
Process flow of machine learning–based prediction models of rat tuberculosis detection performance. The rectangle symbols represent data, while the histogram entails model evaluation metrics. DT: decision tree; kNN: k-nearest neighbor; NB: naïve Bayes; RF: random forest; SVM: support vector machine.
Figure 4
Figure 4
Confusion matrices of the predictive models. (A) Support vector machine classifier; (B) Random forest classifier.
See this image and copyright information in PMC

References

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