Combining expert knowledge and machine-learning to classify herd types in livestock systems

Abstract A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock sys...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jonas Brock, Martin Lange, Jamie A. Tratalos, Simon J. More, David A. Graham, Maria Guelbenzu-Gonzalo, Hans-Hermann Thulke
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f6995493c9c0436dbc03fface3d3257c
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:f6995493c9c0436dbc03fface3d3257c
record_format	dspace
spelling	oai:doaj.org-article:f6995493c9c0436dbc03fface3d3257c2021-12-02T14:06:31ZCombining expert knowledge and machine-learning to classify herd types in livestock systems10.1038/s41598-021-82373-32045-2322https://doaj.org/article/f6995493c9c0436dbc03fface3d3257c2021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82373-3https://doaj.org/toc/2045-2322Abstract A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock systems by combining expert knowledge and a machine-learning algorithm called self-organising-maps (SOMs). This approach is applied to the cattle sector in Ireland, where a detailed understanding of herd types can assist with on-going discussions on control and surveillance for endemic cattle diseases. To our knowledge, this is the first time that the SOM algorithm has been used to differentiate livestock systems. In compliance with European Union (EU) requirements, relevant data in the Irish livestock register includes the birth, movements and disposal of each individual bovine, and also the sex and breed of each bovine and its dam. In total, 17 herd types were identified in Ireland using 9 variables. We provide a data-driven classification tree using decisions derived from the Irish livestock registration data. Because of the visual capabilities of the SOM algorithm, the interpretation of results is relatively straightforward and we believe our approach, with adaptation, can be used to classify herd type in any other livestock system.Jonas BrockMartin LangeJamie A. TratalosSimon J. MoreDavid A. GrahamMaria Guelbenzu-GonzaloHans-Hermann ThulkeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jonas Brock Martin Lange Jamie A. Tratalos Simon J. More David A. Graham Maria Guelbenzu-Gonzalo Hans-Hermann Thulke Combining expert knowledge and machine-learning to classify herd types in livestock systems
description	Abstract A detailed understanding of herd types is needed for animal disease control and surveillance activities, to inform epidemiological study design and interpretation, and to guide effective policy decision-making. In this paper, we present a new approach to classify herd types in livestock systems by combining expert knowledge and a machine-learning algorithm called self-organising-maps (SOMs). This approach is applied to the cattle sector in Ireland, where a detailed understanding of herd types can assist with on-going discussions on control and surveillance for endemic cattle diseases. To our knowledge, this is the first time that the SOM algorithm has been used to differentiate livestock systems. In compliance with European Union (EU) requirements, relevant data in the Irish livestock register includes the birth, movements and disposal of each individual bovine, and also the sex and breed of each bovine and its dam. In total, 17 herd types were identified in Ireland using 9 variables. We provide a data-driven classification tree using decisions derived from the Irish livestock registration data. Because of the visual capabilities of the SOM algorithm, the interpretation of results is relatively straightforward and we believe our approach, with adaptation, can be used to classify herd type in any other livestock system.
format	article
author	Jonas Brock Martin Lange Jamie A. Tratalos Simon J. More David A. Graham Maria Guelbenzu-Gonzalo Hans-Hermann Thulke
author_facet	Jonas Brock Martin Lange Jamie A. Tratalos Simon J. More David A. Graham Maria Guelbenzu-Gonzalo Hans-Hermann Thulke
author_sort	Jonas Brock
title	Combining expert knowledge and machine-learning to classify herd types in livestock systems
title_short	Combining expert knowledge and machine-learning to classify herd types in livestock systems
title_full	Combining expert knowledge and machine-learning to classify herd types in livestock systems
title_fullStr	Combining expert knowledge and machine-learning to classify herd types in livestock systems
title_full_unstemmed	Combining expert knowledge and machine-learning to classify herd types in livestock systems
title_sort	combining expert knowledge and machine-learning to classify herd types in livestock systems
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f6995493c9c0436dbc03fface3d3257c
work_keys_str_mv	AT jonasbrock combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT martinlange combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT jamieatratalos combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT simonjmore combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT davidagraham combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT mariaguelbenzugonzalo combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems AT hanshermannthulke combiningexpertknowledgeandmachinelearningtoclassifyherdtypesinlivestocksystems
_version_	1718391999586893824

Combining expert knowledge and machine-learning to classify herd types in livestock systems

Ejemplares similares