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Machine Learning for Prediction of Tuberculosis Detection: Case Study of Trained African Giant Pouched Rats.
Joan Jonathan1, Alcardo Alex Barakabitze1, Cynthia D Fast2,3,4
1Department of Informatics and Information Technology, Sokoine University of Agriculture, Morogoro, United Republic of Tanzania.
Online Journal of Public Health Informatics
|April 16, 2024
Summary
Machine learning models can predict tuberculosis (TB) detection by rats with 83.39% accuracy. Incorporating sample diagnostic results significantly enhances the performance of these trained rats in TB detection efforts.
Area of Science:
- Veterinary Medicine
- Machine Learning
- Public Health
Background:
- Tuberculosis (TB) diagnosis generates vast amounts of data, necessitating innovative approaches for efficient detection.
- Trained rats offer a cost-effective, sensitive, and rapid method for TB detection, particularly benefiting developing nations.
- Operational research in Tanzania and Ethiopia utilizes TB-detecting rats to complement existing diagnostic tools.
Purpose of the Study:
- To develop machine learning (ML) models for predicting rat performance in TB detection.
- To enhance the diagnostic accuracy and overall performance of rat-based TB detection systems.
- To identify patterns influencing TB detection success using ML techniques.
Main Methods:
- Utilized a dataset of 366,441 observations from 2012-2019 provided by the APOPO Center.
- Applied various ML techniques, including decision tree, random forest, naïve Bayes, support vector machine, and k-nearest neighbor.
- Incorporated variables such as diagnostic results from WHO-endorsed methods at partner health clinics.
Main Results:
- The support vector machine model achieved the highest prediction accuracy at 83.39%.
- Including variables related to the presence of TB in samples significantly improved predictive model performance.
- ML techniques demonstrate potential for optimizing rat-based TB detection.
Conclusions:
- Integrating diagnostic results of TB samples can enhance the detection performance of trained rats.
- Findings can inform TB-detection rat trainers and policymakers to maintain and improve this diagnostic technology.
- Optimizing ML models may further increase the efficacy of rats in TB case detection.
