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...

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Bibliographic Details
Published in:	PloS one 2023-04, Vol.18 (4), p.e0284522-e0284522
Main Authors:	Raza, Ali, Rustam, Furqan, Siddiqui, Hafeez Ur Rehman, Diez, Isabel de la Torre, Ashraf, Imran
Format:	Article
Language:	eng
Subjects:	Accuracy Algorithms Alzheimer's disease Alzheimers disease Analysis Artificial intelligence Bacteria Biology and Life Sciences Classification Classifiers Computer and Information Sciences Data analysis Data entry Datasets Decision trees Deep learning Discriminant analysis Engineering and Technology Evaluation Geometric accuracy Gram-positive bacteria Health risks Human body Humans Identification and classification Machine Learning Malaria Medical research Microbiology Microorganisms Modelling Neural networks Organisms Research and Analysis Methods Seroepidemiologic Studies Serology Staphylococcus infections Support vector machines Toxoplasmosis Vector-borne diseases
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Summary:	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.
ISSN:	1932-6203 1932-6203