Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep
Autor(a) principal: | |
---|---|
Data de Publicação: | 2023 |
Outros Autores: | , , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.3390/ani13030374 http://hdl.handle.net/11449/246808 |
Resumo: | This 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. |
id |
UNSP_7c86c8cf47a06ebdc277112f92441702 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/246808 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheepmultinomial logistic regressionOvis ariesprecisionsensitivityThis 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.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Department of Genetics University of Sao Paulo, SPDepartment of Animal and Dairy Sciences University of WisconsinDepartment of Animal Science Michigan State UniversitySão Paulo Agency of Agribusiness and Technology Animal Science Institute, SPSchool of Agricultural and Veterinary Sciences São Paulo State University, SPSustainable Livestock Research Center Animal Science Institute, SPSchool of Agricultural and Veterinary Sciences São Paulo State University, SPUniversidade de São Paulo (USP)University of WisconsinMichigan State UniversityAnimal Science InstituteUniversidade Estadual Paulista (UNESP)Freitas, Luara A.Savegnago, Rodrigo P.Alves, Anderson A. C.Costa, Ricardo L. D.Munari, Danisio P. [UNESP]Stafuzza, Nedenia B.Rosa, Guilherme J. M.Paz, Claudia C. P.2023-07-29T12:51:00Z2023-07-29T12:51:00Z2023-02-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.3390/ani13030374Animals, v. 13, n. 3, 2023.2076-2615http://hdl.handle.net/11449/24680810.3390/ani130303742-s2.0-85147833385Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAnimalsinfo:eu-repo/semantics/openAccess2023-07-29T12:51:01Zoai:repositorio.unesp.br:11449/246808Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T14:32:47.128133Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
title |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
spellingShingle |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep Freitas, Luara A. multinomial logistic regression Ovis aries precision sensitivity |
title_short |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
title_full |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
title_fullStr |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
title_full_unstemmed |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
title_sort |
Classification Performance of Machine Learning Methods for Identifying Resistance, Resilience, and Susceptibility to Haemonchus contortus Infections in Sheep |
author |
Freitas, Luara A. |
author_facet |
Freitas, Luara A. Savegnago, Rodrigo P. Alves, Anderson A. C. Costa, Ricardo L. D. Munari, Danisio P. [UNESP] Stafuzza, Nedenia B. Rosa, Guilherme J. M. Paz, Claudia C. P. |
author_role |
author |
author2 |
Savegnago, Rodrigo P. Alves, Anderson A. C. Costa, Ricardo L. D. Munari, Danisio P. [UNESP] Stafuzza, Nedenia B. Rosa, Guilherme J. M. Paz, Claudia C. P. |
author2_role |
author author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade de São Paulo (USP) University of Wisconsin Michigan State University Animal Science Institute Universidade Estadual Paulista (UNESP) |
dc.contributor.author.fl_str_mv |
Freitas, Luara A. Savegnago, Rodrigo P. Alves, Anderson A. C. Costa, Ricardo L. D. Munari, Danisio P. [UNESP] Stafuzza, Nedenia B. Rosa, Guilherme J. M. Paz, Claudia C. P. |
dc.subject.por.fl_str_mv |
multinomial logistic regression Ovis aries precision sensitivity |
topic |
multinomial logistic regression Ovis aries precision sensitivity |
description |
This 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. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T12:51:00Z 2023-07-29T12:51:00Z 2023-02-01 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.3390/ani13030374 Animals, v. 13, n. 3, 2023. 2076-2615 http://hdl.handle.net/11449/246808 10.3390/ani13030374 2-s2.0-85147833385 |
url |
http://dx.doi.org/10.3390/ani13030374 http://hdl.handle.net/11449/246808 |
identifier_str_mv |
Animals, v. 13, n. 3, 2023. 2076-2615 10.3390/ani13030374 2-s2.0-85147833385 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Animals |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808128376137842688 |