Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorUniversity of Wisconsin-
Autor(es): dc.contributorMichigan State University-
Autor(es): dc.contributorAnimal Science Institute-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorFreitas, Luara A.-
Autor(es): dc.creatorSavegnago, Rodrigo P.-
Autor(es): dc.creatorAlves, Anderson A. C.-
Autor(es): dc.creatorCosta, Ricardo L. D.-
Autor(es): dc.creatorMunari, Danisio P.-
Autor(es): dc.creatorStafuzza, Nedenia B.-
Autor(es): dc.creatorRosa, Guilherme J. M.-
Autor(es): dc.creatorPaz, Claudia C. P.-
Data de aceite: dc.date.accessioned2025-08-21T15:53:09Z-
Data de disponibilização: dc.date.available2025-08-21T15:53:09Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-01-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/ani13030374-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246808-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246808-
Descrição: dc.descriptionThis study investigated the feasibility of using easy-to-measure phenotypic traits to predict sheep resistant, resilient, and susceptible to gastrointestinal nematodes, compared the classification performance of multinomial logistic regression (MLR), linear discriminant analysis (LDA), random forest (RF), and artificial neural network (ANN) methods, and evaluated the applicability of the best classification model on each farm. The database comprised 3654 records of 1250 Santa Inês sheep from 6 farms. The animals were classified into resistant (2605 records), resilient (939 records), and susceptible (110 records) according to fecal egg count and packed cell volume. A random oversampling method was performed to balance the dataset. The classification methods were fitted using the information of age class, the month of record, farm, sex, Famacha© degree, body weight, and body condition score as predictors, and the resistance, resilience, and susceptibility to gastrointestinal nematodes as the target classes to be predicted considering data from all farms randomly. An additional leave-one-farm-out cross-validation technique was used to assess prediction quality across farms. The MLR and LDA models presented good performances in predicting susceptible and resistant animals. The results suggest that the use of readily available records and easily measurable traits may provide useful information for supporting management decisions at the farm level.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Genetics University of Sao Paulo, SP-
Descrição: dc.descriptionDepartment of Animal and Dairy Sciences University of Wisconsin-
Descrição: dc.descriptionDepartment of Animal Science Michigan State University-
Descrição: dc.descriptionSão Paulo Agency of Agribusiness and Technology Animal Science Institute, SP-
Descrição: dc.descriptionSchool of Agricultural and Veterinary Sciences São Paulo State University, SP-
Descrição: dc.descriptionSustainable Livestock Research Center Animal Science Institute, SP-
Descrição: dc.descriptionSchool of Agricultural and Veterinary Sciences São Paulo State University, SP-
Idioma: dc.languageen-
Relação: dc.relationAnimals-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectmultinomial logistic regression-
Palavras-chave: dc.subjectOvis aries-
Palavras-chave: dc.subjectprecision-
Palavras-chave: dc.subjectsensitivity-
Título: dc.titleClassification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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