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Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review

Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Arti...

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Main Authors:	Balakrishnan, Vimala, Kehrabi, Yousra, Ramanathan, Ghayathri, Paul, Scott Arjay, Tiong, Chiong Kian
Format:	Article
Published:	Elsevier 2023
Subjects:	Q Science (General) QA75 Electronic computers. Computer science
Online Access:	http://eprints.um.edu.my/38418/
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spelling	my.um.eprints.384182023-12-01T13:40:58Z http://eprints.um.edu.my/38418/ Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review Balakrishnan, Vimala Kehrabi, Yousra Ramanathan, Ghayathri Paul, Scott Arjay Tiong, Chiong Kian Q Science (General) QA75 Electronic computers. Computer science Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models. Elsevier 2023-05 Article PeerReviewed Balakrishnan, Vimala and Kehrabi, Yousra and Ramanathan, Ghayathri and Paul, Scott Arjay and Tiong, Chiong Kian (2023) Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review. Progress in Biophysics and Molecular Biology, 179. pp. 16-25. ISSN 00796107, DOI https://doi.org/10.1016/j.pbiomolbio.2023.03.001 <https://doi.org/10.1016/j.pbiomolbio.2023.03.001>. 10.1016/j.pbiomolbio.2023.03.001
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
topic	Q Science (General) QA75 Electronic computers. Computer science
spellingShingle	Q Science (General) QA75 Electronic computers. Computer science Balakrishnan, Vimala Kehrabi, Yousra Ramanathan, Ghayathri Paul, Scott Arjay Tiong, Chiong Kian Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
description	Biomarker-based tests may facilitate Tuberculosis (TB) diagnosis, accelerate treatment initiation, and thus improve outcomes. This review synthesizes the literature on biomarker-based detection for TB diagnosis using machine learning. The systematic review approach follows the PRISMA guideline. Articles were sought using relevant keywords from Web of Science, PubMed, and Scopus, resulting in 19 eligible studies after a meticulous screening. All the studies were found to have focused on the supervised learning approach, with Support Vector Machine (SVM) and Random Forest emerging as the top two algorithms, with the highest accuracy, sensitivity and specificity reported to be 97.0%, 99.2%, and 98.0%, respectively. Further, protein-based biomarkers were widely explored, followed by gene-based such as RNA sequence and, Spoligotypes. Publicly available datasets were observed to be popularly used by the studies reviewed whilst studies targeting specific cohorts such as HIV patients or children gathering their own data from healthcare facilities, leading to smaller datasets. Of these, most studies used the leave one out cross validation technique to mitigate overfitting. The review shows that machine learning is increasingly assessed in research to improve TB diagnosis through biomarkers, as promising results were shown in terms of model's detection performance. This provides insights on the possible application of machine learning approaches to diagnose TB using biomarkers as opposed to the traditional methods that can be time consuming. Low-middle income settings, where access to basic biomarkers could be provided as compared to sputum-based tests that are not always available, could be a major application of such models.
format	Article
author	Balakrishnan, Vimala Kehrabi, Yousra Ramanathan, Ghayathri Paul, Scott Arjay Tiong, Chiong Kian
author_facet	Balakrishnan, Vimala Kehrabi, Yousra Ramanathan, Ghayathri Paul, Scott Arjay Tiong, Chiong Kian
author_sort	Balakrishnan, Vimala
title	Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
title_short	Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
title_full	Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
title_fullStr	Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
title_full_unstemmed	Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review
title_sort	machine learning approaches in diagnosing tuberculosis through biomarkers-a systematic review
publisher	Elsevier
publishDate	2023
url	http://eprints.um.edu.my/38418/
_version_	1784511840207765504
score	13.211869

Machine learning approaches in diagnosing tuberculosis through biomarkers-A systematic review

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