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Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning

Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four ma...

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Autores principales: Ferreira, Tiago S., Santana, Ewaldo E. C., Jacob Junior, Antônio F. L., Silva Junior, Paulo F., Bastos, Luciana S., Silva, Ana L. A., Melo, Solange A., Cruz, Carlos A. M., Aquino, Vivianne S., Castro, Luís S. O., Lima, Guilherme O., Freire, Raimundo C. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105265/
https://www.ncbi.nlm.nih.gov/pubmed/35590819
http://dx.doi.org/10.3390/s22093128
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author Ferreira, Tiago S.
Santana, Ewaldo E. C.
Jacob Junior, Antônio F. L.
Silva Junior, Paulo F.
Bastos, Luciana S.
Silva, Ana L. A.
Melo, Solange A.
Cruz, Carlos A. M.
Aquino, Vivianne S.
Castro, Luís S. O.
Lima, Guilherme O.
Freire, Raimundo C. S.
author_facet Ferreira, Tiago S.
Santana, Ewaldo E. C.
Jacob Junior, Antônio F. L.
Silva Junior, Paulo F.
Bastos, Luciana S.
Silva, Ana L. A.
Melo, Solange A.
Cruz, Carlos A. M.
Aquino, Vivianne S.
Castro, Luís S. O.
Lima, Guilherme O.
Freire, Raimundo C. S.
author_sort Ferreira, Tiago S.
collection PubMed
description Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four machine learning models were chosen: K-nearest neighbor, Naïve Bayes, support vector machine and logistic regression models. The tests were performed on three hundred and forty dogs, using eighteen characteristics of the animal and the ELISA (enzyme-linked immunosorbent assay) serological test as validation. Logistic regression achieved the best metrics: Accuracy of 75%, sensitivity of 84%, specificity of 67%, a positive likelihood ratio of 2.53 and a negative likelihood ratio of 0.23, showing a positive relationship in the evaluation between the true positives and rejecting the cases of false negatives.
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spelling pubmed-91052652022-05-14 Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning Ferreira, Tiago S. Santana, Ewaldo E. C. Jacob Junior, Antônio F. L. Silva Junior, Paulo F. Bastos, Luciana S. Silva, Ana L. A. Melo, Solange A. Cruz, Carlos A. M. Aquino, Vivianne S. Castro, Luís S. O. Lima, Guilherme O. Freire, Raimundo C. S. Sensors (Basel) Communication Proposal techniques that reduce financial costs in the diagnosis and treatment of animal diseases are welcome. This work uses some machine learning techniques to classify whether or not cases of canine visceral leishmaniasis are present by physical examinations. For validation of the method, four machine learning models were chosen: K-nearest neighbor, Naïve Bayes, support vector machine and logistic regression models. The tests were performed on three hundred and forty dogs, using eighteen characteristics of the animal and the ELISA (enzyme-linked immunosorbent assay) serological test as validation. Logistic regression achieved the best metrics: Accuracy of 75%, sensitivity of 84%, specificity of 67%, a positive likelihood ratio of 2.53 and a negative likelihood ratio of 0.23, showing a positive relationship in the evaluation between the true positives and rejecting the cases of false negatives. MDPI 2022-04-20 /pmc/articles/PMC9105265/ /pubmed/35590819 http://dx.doi.org/10.3390/s22093128 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Communication
Ferreira, Tiago S.
Santana, Ewaldo E. C.
Jacob Junior, Antônio F. L.
Silva Junior, Paulo F.
Bastos, Luciana S.
Silva, Ana L. A.
Melo, Solange A.
Cruz, Carlos A. M.
Aquino, Vivianne S.
Castro, Luís S. O.
Lima, Guilherme O.
Freire, Raimundo C. S.
Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title_full Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title_fullStr Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title_full_unstemmed Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title_short Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning
title_sort diagnostic classification of cases of canine leishmaniasis using machine learning
topic Communication
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9105265/
https://www.ncbi.nlm.nih.gov/pubmed/35590819
http://dx.doi.org/10.3390/s22093128
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