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Comparison between data mining methods to assess calving difficulty in cattle

Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to the... Full description

Journal Title: Revista Colombiana de Ciencias Pecuarias Vol.30(3), pp.196-208
Main Author: Daniel Zaborski
Other Authors: Witold S Proskura , Wilhelm Grzesiak
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0120-0690 ; DOI: 10.17533/udea.rccp.v30n3a03
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recordid: doaj_soai_doaj_org_article_cf27b85d6cea4dec8ce76c8461ad5781
title: Comparison between data mining methods to assess calving difficulty in cattle
format: Article
creator:
  • Daniel Zaborski
  • Witold S Proskura
  • Wilhelm Grzesiak
subjects:
  • Classification
  • Dairy Heifers
  • Decision Support Systems
  • Dystocia
  • Electronic Learning
  • Veterinary Medicine
ispartof: Revista Colombiana de Ciencias Pecuarias, Vol.30(3), pp.196-208
description: Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull’s rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.
language: eng
source:
identifier: ISSN: 0120-0690 ; DOI: 10.17533/udea.rccp.v30n3a03
fulltext: fulltext_linktorsrc
issn:
  • 0120-0690
  • 01200690
url: Link


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titleComparison between data mining methods to assess calving difficulty in cattle
creatorDaniel Zaborski ; Witold S Proskura ; Wilhelm Grzesiak
ispartofRevista Colombiana de Ciencias Pecuarias, Vol.30(3), pp.196-208
identifierISSN: 0120-0690 ; DOI: 10.17533/udea.rccp.v30n3a03
subjectClassification ; Dairy Heifers ; Decision Support Systems ; Dystocia ; Electronic Learning ; Veterinary Medicine
descriptionAbstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull’s rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.
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Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull’s rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.

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Abstract Background: Dystocia in cattle results in adverse consequences (increased calf morbidity and mortality, decreased fertility, and milk production, lower cow survival and reduced welfare) leading to considerable economic losses. Objective: To classify calvings in dairy cattle according to their difficulty using selected data mining methods (classification and regression trees (CART), chi-square automatic interaction detection trees (CHAID) and quick, unbiased, efficient, statistical trees (QUEST)), and to identify the most significant factors affecting calving difficulty. The results of data mining methods were compared with those of a more traditional generalized linear model (GLM). Methods: A total of 1,342 calving records of Polish Holstein- Friesian black-and-white heifers from four farms were used. Calving difficulty was divided into three categories (easy, moderate and difficult). Results: The percentages of calvings correctly classified by CART, CHAID, QUEST, and GLM were as follows: 35.14, 18.92, 19.82, and 43.24% (easy), 68.70, 73.91, 81.74, and 41.74% (moderate), and 77.27, 85.45, 73.64, and 81.82% (difficult), respectively. The most important factors affecting calving difficulty were bull’s rank (based on the mean calving difficulty score of its daughters), calving age, farm category (based on its mean milk yield) and calving season. Conclusion: All classification models were satisfactory and could predict the class of calving difficulty.

pubUniversidad de Antioquia
doi10.17533/udea.rccp.v30n3a03
urlhttps://doaj.org/article/cf27b85d6cea4dec8ce76c8461ad5781
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