Title:	Machine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks.
Authors:	NICIURA, S. C. M. SANCHES, G. M.
Affiliation:	SIMONE CRISTINA MEO NICIURA, CPPSE; GUILHERME MARTINELI SANCHES, Universidade de São Paulo.
Date Issued:	2024
Citation:	Revista Brasileira de Parasitologia Veterinária, v. 33, n. 1, jan./mar. 2024.
Description:	The 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.
NAL Thesaurus:	Carts Gastrointestinal nematodes
Keywords:	Machine learning Multidrug resistance Random forest
DOI:	10.1590/S1984-29612024014
Type of Material:	Artigo de periódico
Access:	openAccess
Appears in Collections:	Artigo em periódico indexado (CPPSE)