Machine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks.

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dc.contributorSIMONE CRISTINA MEO NICIURA, CPPSE; GUILHERME MARTINELI SANCHES, Universidade de São Paulo.
dc.creatorNICIURA, S. C. M.
dc.creatorSANCHES, G. M.
dc.date2024-03-18T18:32:33Z
dc.date2024-03-18T18:32:33Z
dc.date2024-03-18
dc.date2024
dc.date.accessioned2026-07-07T05:33:25Z
dc.descriptionThe high prevalence of Haemonchus contortus and its anthelmintic resistance have affected sheep production worldwide. Machine learning approaches are able to investigate the complex relationships among the factors involved in resistance. Classification trees were built to predict multidrug resistance from 36 management practices in 27 sheep flocks. Resistance to five anthelmintics was assessed using a fecal egg count reduction test (FECRT), and 20 flocks with FECRT < 80% for four or five anthelmintics were considered resistant. The data were randomly split into training (75%) and test (25%) sets, resampled 1,000 times, and the classification trees were generated for the training data. Of the 1,000 trees, 24 (2.4%) showed 100% accuracy, sensitivity, and specificity in predicting a flock as resistant or susceptible for the test data. Forage species was a split common to all 24 trees, and the most frequent trees (12/24) were split by forage species, grazing pasture area, and fecal examination. The farming system, Suffolk sheep breed, and anthelmintic choice criteria were practices highlighted in the other trees. These management practices can be used to predict the anthelmintic resistance status and guide measures for gastrointestinal nematode control in sheep flocks.
dc.identifierRevista Brasileira de Parasitologia Veterinária, v. 33, n. 1, jan./mar. 2024.
dc.identifierhttp://www.alice.cnptia.embrapa.br/alice/handle/doc/1162930
dc.identifier10.1590/S1984-29612024014
dc.identifier.urihttp://hdl.handle.net/123456789/494893
dc.languageeng
dc.rightsopenAccess
dc.subjectMachine learning
dc.subjectMultidrug resistance
dc.subjectRandom forest
dc.subjectCarts
dc.subjectGastrointestinal nematodes
dc.titleMachine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks.
dc.typeArtigo de periódico

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