Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries

Journal article

Karmani, P., Chandio, A. A., Korejo, I. A., Samuel, O. and Aborokbah, M. 2024. Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries. Peer J Computer Science . pp. 1-20. https://doi.org/10.7717/peerj-cs.2397
AuthorsKarmani, P., Chandio, A. A., Korejo, I. A., Samuel, O. and Aborokbah, M.
Abstract

Background: Tuberculosis (TB) remains one of the top infectious killers in the world and a prominent fatal disease in developing countries. This study proposes a
prototypical solution to early prevention of TB based on its primary symptoms, signs, and risk factors, implemented by means of machine learning (ML) predictive algorithms. Further novelty of the study lies in the uniqueness of patient dataset collected from three top-ranked hospitals of Sindh, Pakistan, via a self-administered survey patient-records that comprises a set of questions asked by the doctors treating
TB patients in real-time. A total of 1,200 survey patient-records were evenly distributed among all three hospitals, viz. ICT Kotri, LUMHS Jamshoro, and Civil Hospital Hyderabad.

Methods: To develop the required prototypes, the research made use of five distinct benchmark ML algorithms: decision tree (DT), Gaussian naive Bayes (GNB), logistic
regression classifier (LRC), adaptive boosting (AdaBoost), and neural network (NN), whose performance was evaluated by considering various performance metrics, i.e., accuracy, precision, recall, F1 score, and confusion matrix.

Results: The experimental results, graphically visualized and systematically discoursed, demonstrate that early detection of TB classifiers, including DT, GNB, LRC, AdaBoost, and NN, attained accuracy rates of 92.11%, 89.04%, 90.35%, 93.42%,
and 92.98%, respectively. These results indicate effective diagnosis of TB disease by each implemented ML algorithm.

KeywordsTuberculosis (TB) diagnosis; Machine learning (ML); Good health and well-being; Optimal ML model for TB diagnosis
Year2024
JournalPeer J Computer Science
Journal citationpp. 1-20
PublisherPeerJ
ISSN2376-5992
Digital Object Identifier (DOI)https://doi.org/10.7717/peerj-cs.2397
Web address (URL)https://peerj.com/articles/cs-2397/#
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Output statusPublished
Publication dates
Online16 Oct 2024
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Deposited20 Nov 2024
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