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Title:           Applicability of machine learning methods for classifying lightweight pigs in commercial conditions
Subject:         DOI: 10.1093/tas/txae171
Translational Animal Science, Volume. 8, Issue. XX, 5 December 2024
Abstract 
The varying growth rates within a group of pigs present a significant challenge for the current all-in-all-out systems in the pig industry. This study evaluated the applicability of statistical methods for classifying pigs at risk of growth retardation at different production stages using a robust dataset collected under commercial conditions. Data from 26,749 crossbred pigs (Yorkshire × Landrace) with Duroc at weaning (17 to 27 d), 15,409 pigs at the end of the nursery period (60 to 78 d), and 4996 pigs at slaughter (151 to 161 d) were analyzed under three different cut points (lowest 10%, 20%, and 30% weights) to characterize light animals. Records were randomly split into training and testing sets in a 2:1 ratio, and each training dataset was analyzed using an ordinary least squares approach and three machine learning algorithms (decision tree, random forest, and generalized boosted regression). The classification performance of each analytical approach was evaluated by the area under the curve (AUC). In all production stages and cut points, the random forest and generalized boosted regression models demonstrated superior classification performance, with AUC estimates ranging from 0.772 to 0.861. The parametric linear model also showed acceptable classification performance, with slightly lower AUC estimates ranging from 0.752 to 0.818. In contrast, the single decision tree was categorized as worthless, with AUC estimates between 0.608 and 0.726. Key prediction factors varied across production stages, with birthweight-related factors being most significant at weaning, and weight at previous stages becoming more crucial later in the production cycle. These findings suggest the potential of machine learning algorithms to improve decision-making and efficiency in pig production systems by accurately identifying pigs at risk of growth retardation.
Author:          Pau Salgado-López,,Joaquim Casellas,,Iara Solar Diaz,,Thomas Rathje,,Josep Gasa,,David Solà-Oriol
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     Item: 
      Title: Applicability of machine learning methods for classifying lightweight pigs in commercial conditions
      Children: 
       Item: 
        Title: INTRODUCTION
       Item: 
        Title: MATERIALS AND METHODS
        Children: 
         Item: 
          Title: Commercial Pig Farm Data
         Item: 
          Title: Operational Models
         Item: 
          Title: Weighting Function
         Item: 
          Title: Analytical Approaches and Model Hyperparameter Optimization
         Item: 
          Title: Cross-Validation for Classification Ability
       Item: 
        Title: RESULTS
        Children: 
         Item: 
          Title: Comparison of Classification Ability Across Models
         Item: 
          Title: Importance of Prediction Factors
       Item: 
        Title: DISCUSSION
       Item: 
        Title: CONCLUSION
       Item: 
        Title: Acknowledgments
       Item: 
        Title: Literature Cited
   Info: 
    Author: Pau Salgado-López,,Joaquim Casellas,,Iara Solar Diaz,,Thomas Rathje,,Josep Gasa,,David Solà-Oriol
    Subject: DOI: 10.1093/tas/txae171
Translational Animal Science, Volume. 8, Issue. XX, 5 December 2024
Abstract 
The varying growth rat攀猀 眀椀琀栀椀渀 愀 最爀漀甀瀀 漀昀 瀀椀最猀 瀀爀攀猀攀渀琀 愀 猀椀最渀椀昀椀挀愀渀琀 挀栀愀氀氀攀渀最攀 昀漀爀 琀栀攀 挀甀爀爀攀渀琀 愀氀氀ⴀ椀渀ⴀ愀氀氀ⴀ漀甀琀 猀礀猀琀攀洀猀 椀渀 琀栀攀 瀀椀最 椀渀搀甀猀琀爀礀⸀ 吀栀椀猀 猀琀甀搀 evaluated the applicability of statistical methods for classifying pigs at risk of growth retardation at different production 猀琀愀最攀猀 甀猀椀渀最 愀 爀漀戀甀猀琀 搀愀琀愀猀攀琀 挀漀氀氀攀挀琀攀搀 甀渀搀攀爀 挀漀洀洀攀爀挀椀愀氀 挀漀渀搀椀琀椀漀渀猀⸀ 䐀愀琀愀 昀爀漀洀 ㈀㘀Ⰰ㜀㐀㤀 挀爀漀猀猀戀爀攀搀 瀀椀最猀 夀漀爀欀猀栀椀爀攠Ԁ휠Ԁ䰀愀渀搀爀愀挀攀 眀椀h Duroc at weaning (17 to 27 d), 15,409 pigs at the end of the nursery period (60 to 78 d), and 4996 pigs at slaughter (151 to 161 d) were analyzed under three different cut points (lowest 10%, 20%, and 30% weights) to characterize light animals. Records were randomly split into training and testing sets in a 2:1 ratio, and each training dataset was analyzed using an ordinary 氀攀愀猀琀 猀焀甀愀爀攀猀 愀瀀瀀爀漀愀挀栀 愀渀搀 琀栀爀攀攀 洀愀挀栀椀渀攀 氀攀愀爀渀椀渀最 愀氀最漀爀椀琀栀洀猀 搀攀挀椀猀椀漀渀 琀爀攀攀Ⰰ 爀愀渀搀漀洀 昀漀爀攀猀琀Ⰰ 愀渀搀 最攀渀攀爀愀氀椀稀攀搀 戀漀漀猀琀攀搀 爀攀最爀攀猀猀椀漀渀⸀ 吀栀攀 挀氀愀猀猀椀昀椀挀愀琀椀漀渀 瀀攀爀昀漀爀洀愀渀挀攀 漀昀 攀愀挀栀 愀渀愀氀礀琀椀挀愀氀 愀瀀瀀爀漀愀挀栀 眀愀猀 攀瘀愀氀甀愀琀攀搀 戀礀 琀栀攀 愀爀攀愀 甀渀搀攀爀 琀栀攀 挀甀爀瘀攀 䄀唀䌀⸀ 䤀渀 愀氀氀 瀀爀漀搀甀挀琀椀漀渀 猀琀愀最攀猀 愀渀搀 挀甀琀 瀀漀椀渀琀猀Ⰰ 琀栀攀 爀愀渀搀漀洀 昀漀爀攀猀琀 愀渀搀 最攀渀攀爀愀氀椀稀攀搀 戀漀漀猀琀攀搀 爀攀最爀攀猀猀椀漀渀 洀漀搀攀氀猀 搀攀洀漀渀猀琀爀愀琀攀搀 猀甀瀀攀爀椀漀爀 挀氀愀猀猀椀昀椀挀愀琀椀漀渀 瀀攀爀昀rmance, with AUC estimates ranging from 0.772 to 0.861. The parametric linear model also showed acceptable classification perfo爀洀愀渀挀攀Ⰰ 眀椀琀栀 猀氀椀最栀琀氀礀 氀漀眀攀爀 䄀唀䌀 攀猀琀椀洀愀琀攀猀 爀愀渀最椀渀最 昀爀漀洀 　⸀㜀㔀㈀ 琀漀 　⸀㠀㄀㠀⸀ 䤀渀 挀漀渀琀爀愀猀琀Ⰰ 琀栀攀 猀椀渀最氀攀 搀攀挀椀猀椀漀渀 琀爀攀攀 眀愀猀 挀愀琀攀最漀爀椀稀攀搀 愀猀worthless, with AUC estimates between 0.608 and 0.726. Key prediction factors varied across production stages, with birthweightⴀ爀攀氀愀琀攀搀 昀愀挀琀漀爀猀 戀攀椀渀最 洀漀猀琀 猀椀最渀椀昀椀挀愀渀琀 愀琀 眀攀愀渀椀渀最Ⰰ 愀渀搀 眀攀椀最栀琀 愀琀 瀀爀攀瘀椀漀甀猀 猀琀愀最攀猀 戀攀挀漀洀椀渀最 洀漀爀攀 挀爀甀挀椀愀氀 氀愀琀攀爀 椀渀 琀栀攀 瀀爀漀搀甀挀琀椀漀渀cycle. These findings suggest the potential of machine learning algorithms to improve decision-making and efficiency in pig pro搀甀挀琀椀漀渀 猀礀猀琀攀洀猀 戀礀 愀挀挀甀爀愀琀攀氀礀 椀搀攀渀琀椀昀礀椀渀最 瀀椀最猀 愀琀 爀椀猀欀 漀昀 最爀漀眀琀栀 爀攀琀愀爀搀愀琀椀漀渀⸩⽔楴汥⡁灰汩捡扩汩瑹映浡捨楮攠汥慲湩湧整桯摳⁦潲⁣污獳楦祩湧楧桴睥楧桴⁰楧猠楮⁣潭浥牣楡氠捯湤楴楯湳
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               <rdf:li xml:lang="x-default">DOI: 10.1093/tas/txae171&#xD;Translational Animal Science, Volume. 8, Issue. XX, 5 December 2024&#xD;Abstract &#xD;The varying growth rates within a group of pigs present a significant challenge for the current all-in-all-out systems in the pig industry. This study evaluated the applicability of statistical methods for classifying pigs at risk of growth retardation at different production stages using a robust dataset collected under commercial conditions. Data from 26,749 crossbred pigs (Yorkshire × Landrace) with Duroc at weaning (17 to 27 d), 15,409 pigs at the end of the nursery period (60 to 78 d), and 4996 pigs at slaughter (151 to 161 d) were analyzed under three different cut points (lowest 10%, 20%, and 30% weights) to characterize light animals. Records were randomly split into training and testing sets in a 2:1 ratio, and each training dataset was analyzed using an ordinary least squares approach and three machine learning algorithms (decision tree, random forest, and generalized boosted regression). The classification performance of each analytical approach was evaluated by the area under the curve (AUC). In all production stages and cut points, the random forest and generalized boosted regression models demonstrated superior classification performance, with AUC estimates ranging from 0.772 to 0.861. The parametric linear model also showed acceptable classification performance, with slightly lower AUC estimates ranging from 0.752 to 0.818. In contrast, the single decision tree was categorized as worthless, with AUC estimates between 0.608 and 0.726. Key prediction factors varied across production stages, with birthweight-related factors being most significant at weaning, and weight at previous stages becoming more crucial later in the production cycle. These findings suggest the potential of machine learning algorithms to improve decision-making and efficiency in pig production systems by accurately identifying pigs at risk of growth retardation.</rdf:li>
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   Type: CRC32
  Checksum: b78ebfb9d071b80a484ca1bdfeb010ed
   Type: MD5
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   Type: SHA-1
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   Type: SHA-256