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. 2022 Apr 20;22(9):3128.
doi: 10.3390/s22093128.

Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning

Tiago S Ferreira  1 , Ewaldo E C Santana  1 , Antônio F L Jacob Junior  1 , Paulo F Silva Junior  1 , Luciana S Bastos  2 , Ana L A Silva  2 , Solange A Melo  3 , Carlos A M Cruz  4 , Vivianne S Aquino  4 , Luís S O Castro  4 , Guilherme O Lima  5 , Raimundo C S Freire  6
Affiliations

Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning

Tiago S Ferreira et al. Sensors (Basel). .

Abstract

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.

Keywords: canine visceral leishmaniasis; classification; logistic regression; machine learning.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Region of Maranhão, Brazil, where the data were collected.
Figure 2
Figure 2
ROC curve for applying the LR model on the test set.
See this image and copyright information in PMC

References

    1. Cadernos de Saúde Pública DATASUS. [(accessed on 29 September 2021)];2020 Available online: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?sinannet/cnv/leishvma.def.
    1. World Health Organization Leishimaniasis. [(accessed on 5 January 2022)]. Available online: https://www.who.int/data/gho/data/themes/topics/topic-details/GHO/leishm....
    1. Catão R.C. Dengue No Brasil: Abordagem Geográfica na Escala Nacional. Cultura Acadêmica; São Paulo, Brazil: 2012.
    1. Siquera S.C.F. Master’s Thesis. Universidade Federal de Mato Grosso; Cuiabá, Brazil: 2011. Análise Espacial da Dengue no Estado de Mato Grosso no Período de 2007 A 2009.
    1. Bishop C.M. Pattern Recognition and Machine Learning. Springer; Berlin/Heidelberg, Germany: 2006.

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