Data/hora: 22/09/2024 07:56:44

Biblioteca(s): 	Embrapa Pecuária Sudeste.	
Data corrente: 	18/03/2024	
Data da última atualização: 	18/03/2024	
Autoria: 	NICIURA, S. C. M.; SANCHES, G. M.	
Afiliação: 	SIMONE CRISTINA MEO NICIURA, CPPSE; GUILHERME MARTINELI SANCHES, Universidade de São Paulo.	
Título: 	Machine learning prediction of multiple anthelmintic resistance and gastrointestinal nematode control in sheep flocks.	
Ano de publicação: 	2024	
Fonte/Imprenta: 	Revista Brasileira de Parasitologia Veterinária, v. 33, n. 1, jan./mar. 2024.	
DOI: 	10.1590/S1984-29612024014	
Idioma: 	Inglês	
Conteúdo: 	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.	
Palavras-Chave: 	Machine learning; Multidrug resistance; Random forest.	
Thesaurus Nal: 	Carts; Gastrointestinal nematodes.	
Categoria do assunto: 	H Saúde e Patologia	
URL: 	http://ainfo.cnptia.embrapa.br/digital/bitstream/doc/1162930/1/MachineLearningPrediction.pdf	
Registro original: 	Embrapa Pecuária Sudeste (CPPSE)	
Exemplares: 	CPPSE - (26358-1) - UPC - AP - DD - PROCI-2024.00016 - NIC - 2024.00065 - DIS