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Diagnostic Classification of Cases of Canine Leishmaniasis Using Machine Learning.

Tiago S Ferreira1, Ewaldo E C Santana1, Antônio F L Jacob Junior1

  • 1Graduating Program in Computation Engineering and Systems, State University of Maranhão, São Luís 65690-000, Brazil.

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|May 20, 2022
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Summary

Machine learning models can aid in diagnosing canine visceral leishmaniasis using physical exams. Logistic regression showed the best performance, improving diagnostic accuracy for this animal disease.

Keywords:
canine visceral leishmaniasisclassificationlogistic regressionmachine learning

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Area of Science:

  • Veterinary Medicine
  • Machine Learning
  • Epidemiology

Background:

  • Canine visceral leishmaniasis (CVL) poses a significant health and economic burden.
  • Accurate and cost-effective diagnostic methods are crucial for disease control.
  • Early detection through physical examination can improve treatment outcomes.

Purpose of the Study:

  • To evaluate machine learning models for classifying canine visceral leishmaniasis based on physical examination data.
  • To compare the performance of K-nearest neighbor, Naïve Bayes, support vector machine, and logistic regression models.
  • To identify a cost-effective approach for CVL diagnosis.

Main Methods:

  • A dataset of 340 dogs was analyzed, utilizing 18 physical characteristics.
  • Four machine learning algorithms were employed: K-nearest neighbor, Naïve Bayes, support vector machine, and logistic regression.
  • Model performance was validated against the ELISA (enzyme-linked immunosorbent assay) serological test.

Main Results:

  • Logistic regression demonstrated the highest accuracy (75%) and sensitivity (84%).
  • The model achieved a specificity of 67%, a positive likelihood ratio of 2.53, and a negative likelihood ratio of 0.23.
  • Logistic regression effectively identified true positives and reduced false negatives.

Conclusions:

  • Machine learning, particularly logistic regression, offers a promising tool for diagnosing canine visceral leishmaniasis using physical examination data.
  • This approach has the potential to reduce diagnostic costs and improve early detection rates.
  • Further research can refine these models for broader application in animal disease management.