Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis

Abstract Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing th...

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Autores principales:	Victor Chukwudi Osamor, Adaugo Fiona Okezie
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/350b98fea6bf48bbbfd0461e495d2d43
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spelling	oai:doaj.org-article:350b98fea6bf48bbbfd0461e495d2d432021-12-02T17:55:09ZEnhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis10.1038/s41598-021-94347-62045-2322https://doaj.org/article/350b98fea6bf48bbbfd0461e495d2d432021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-94347-6https://doaj.org/toc/2045-2322Abstract Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to reduce the mortality rate of the infection. Despite several methods that exist in diagnosing tuberculosis, the limitations ranging from the cost in carrying out the test to the time taken to obtain the results have hindered early diagnosis of the disease. This work aims to develop a predictive model that would help in the diagnosis of TB using an extended weighted voting ensemble method. The method used to carry out this research involved analyzing tuberculosis gene expression data obtained from GEO (Transcript Expression Omnibus) database and developing a classification model to aid tuberculosis diagnosis. A classifier combination of Naïve Bayes (NB), and Support Vector Machine (SVM) was used to develop the classification model. The weighted voting ensemble technique was used to improve the classification model's performance by combining the classification results of the single classifier and selecting the group with the highest vote based on the weights given to the single classifiers. Experimental analysis indicates a performance accuracy of the enhanced ensemble classifier as 0.95, which showed a better performance than the single classifiers, which had 0.92, and 0.87 obtained from SVM and NB, respectively. The developed model can also assist health practitioners in the timely diagnosis of tuberculosis, which would reduce the mortality rate caused by the disease, especially in developing countries.Victor Chukwudi OsamorAdaugo Fiona OkezieNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Victor Chukwudi Osamor Adaugo Fiona Okezie Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
description	Abstract Tuberculosis has the most considerable death rate among diseases caused by a single micro-organism type. The disease is a significant issue for most third-world countries due to poor diagnosis and treatment potentials. Early diagnosis of tuberculosis is the most effective way of managing the disease in patients to reduce the mortality rate of the infection. Despite several methods that exist in diagnosing tuberculosis, the limitations ranging from the cost in carrying out the test to the time taken to obtain the results have hindered early diagnosis of the disease. This work aims to develop a predictive model that would help in the diagnosis of TB using an extended weighted voting ensemble method. The method used to carry out this research involved analyzing tuberculosis gene expression data obtained from GEO (Transcript Expression Omnibus) database and developing a classification model to aid tuberculosis diagnosis. A classifier combination of Naïve Bayes (NB), and Support Vector Machine (SVM) was used to develop the classification model. The weighted voting ensemble technique was used to improve the classification model's performance by combining the classification results of the single classifier and selecting the group with the highest vote based on the weights given to the single classifiers. Experimental analysis indicates a performance accuracy of the enhanced ensemble classifier as 0.95, which showed a better performance than the single classifiers, which had 0.92, and 0.87 obtained from SVM and NB, respectively. The developed model can also assist health practitioners in the timely diagnosis of tuberculosis, which would reduce the mortality rate caused by the disease, especially in developing countries.
format	article
author	Victor Chukwudi Osamor Adaugo Fiona Okezie
author_facet	Victor Chukwudi Osamor Adaugo Fiona Okezie
author_sort	Victor Chukwudi Osamor
title	Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
title_short	Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
title_full	Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
title_fullStr	Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
title_full_unstemmed	Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
title_sort	enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/350b98fea6bf48bbbfd0461e495d2d43
work_keys_str_mv	AT victorchukwudiosamor enhancingtheweightedvotingensemblealgorithmfortuberculosispredictivediagnosis AT adaugofionaokezie enhancingtheweightedvotingensemblealgorithmfortuberculosispredictivediagnosis
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Enhancing the weighted voting ensemble algorithm for tuberculosis predictive diagnosis

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