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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10118187 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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