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Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier

Microbe organisms make up approximately 60% of the earth’s living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in human...

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Autores principales: Raza, Ali, Rustam, Furqan, Siddiqui, Hafeez Ur Rehman, Diez, Isabel de la Torre, Ashraf, Imran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118187/
https://www.ncbi.nlm.nih.gov/pubmed/37079536
http://dx.doi.org/10.1371/journal.pone.0284522
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author Raza, Ali
Rustam, Furqan
Siddiqui, Hafeez Ur Rehman
Diez, Isabel de la Torre
Ashraf, Imran
author_facet Raza, Ali
Rustam, Furqan
Siddiqui, Hafeez Ur Rehman
Diez, Isabel de la Torre
Ashraf, Imran
author_sort Raza, Ali
collection PubMed
description Microbe organisms make up approximately 60% of the earth’s living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict microbe organisms in the human body. A novel hybrid microbes classifier (HMC) is proposed in this study which is based on a decision tree classifier and extra tree classifier using voting criteria. Experiments involve different machine learning and deep learning models for detecting ten different living microforms of life. Results suggest that the proposed HMC approach achieves a 98% accuracy score, 98% geometric mean score, 97% precision score, and 97% Cohen Kappa score. The proposed model outperforms employed models, as well as, existing state-of-the-art models. Moreover, the k-fold cross-validation corroborates the results as well. The research helps microbiologists identify the type of microbe organisms with high accuracy and prevents many diseases through early detection.
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spelling pubmed-101181872023-04-21 Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier Raza, Ali Rustam, Furqan Siddiqui, Hafeez Ur Rehman Diez, Isabel de la Torre Ashraf, Imran PLoS One Research Article Microbe organisms make up approximately 60% of the earth’s living matter and the human body is home to millions of microbe organisms. Microbes are microbial threats to health and may lead to several diseases in humans like toxoplasmosis and malaria. The microbiological toxoplasmosis disease in humans is widespread, with a seroprevalence of 3.6-84% in sub-Saharan Africa. This necessitates an automated approach for microbe organisms detection. The primary objective of this study is to predict microbe organisms in the human body. A novel hybrid microbes classifier (HMC) is proposed in this study which is based on a decision tree classifier and extra tree classifier using voting criteria. Experiments involve different machine learning and deep learning models for detecting ten different living microforms of life. Results suggest that the proposed HMC approach achieves a 98% accuracy score, 98% geometric mean score, 97% precision score, and 97% Cohen Kappa score. The proposed model outperforms employed models, as well as, existing state-of-the-art models. Moreover, the k-fold cross-validation corroborates the results as well. The research helps microbiologists identify the type of microbe organisms with high accuracy and prevents many diseases through early detection. Public Library of Science 2023-04-20 /pmc/articles/PMC10118187/ /pubmed/37079536 http://dx.doi.org/10.1371/journal.pone.0284522 Text en © 2023 Raza et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Raza, Ali
Rustam, Furqan
Siddiqui, Hafeez Ur Rehman
Diez, Isabel de la Torre
Ashraf, Imran
Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title_full Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title_fullStr Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title_full_unstemmed Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title_short Predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
title_sort predicting microbe organisms using data of living micro forms of life and hybrid microbes classifier
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10118187/
https://www.ncbi.nlm.nih.gov/pubmed/37079536
http://dx.doi.org/10.1371/journal.pone.0284522
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