PUBLICATION DATE

2012-08-07

TITLE

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

ISSUE

73

VOLUME

9

ISSN (print)

N/A

ISSN (electronic)

N/A

ABSTRACT

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

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JOURNAL BRAND

N/A (note: articles not published by Springer Nature have limited metadata)


FROM GRANT

  • The Application Of Support Vector Machine Feature Selection To Cross Sectional Studies In Epidemiology
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    15 TRIPLES      15 PREDICATES      16 URIs      11 LITERALS

    Subject Predicate Object
    1 articles:6e70486c66d0072266207ebe08e343f0 sg:abstract Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.
    2 sg:doi 10.1098/rsif.2011.0852
    3 sg:doiLink http://dx.doi.org/10.1098/rsif.2011.0852
    4 sg:isFundedPublicationOf grants:3118c2d53e1439f05f39d8e2426207e1
    5 sg:issue 73
    6 sg:language English
    7 sg:license http://scigraph.springernature.com/explorer/license/
    8 sg:publicationDate 2012-08-07
    9 sg:publicationYear 2012
    10 sg:publicationYearMonth 2012-08
    11 sg:scigraphId 6e70486c66d0072266207ebe08e343f0
    12 sg:title Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.
    13 sg:volume 9
    14 rdf:type sg:Article
    15 rdfs:label Article: Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.
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