Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed
The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For...
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| creator | Tırınk, Cem Önder, Hasan Francois, Dominique Marcon, Didier Şen, Uğur Shaikenova, Kymbat Omarova, Karlygash Tyasi, Thobela Louis |
| description | The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France. |
| doi_str_mv | 10.1371/journal.pone.0289348 |
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For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0289348</identifier><identifier>PMID: 37535638</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Algorithms ; Analysis ; Animal biology ; Animals ; Artificial intelligence ; Biology and Life Sciences ; Birth weight ; Body weight ; Classification ; Computer and Information Sciences ; Computer Science ; Data mining ; Datasets ; Evaluation ; Females ; Genetic improvement ; Learning algorithms ; Life Sciences ; Machine learning ; Mars ; Modeling and Simulation ; People and Places ; Physical Sciences ; Physiological aspects ; Prediction models ; Regression analysis ; Research and Analysis Methods ; Sex ; Sheep ; Statistical methods ; Suckling behavior ; Support vector machines ; Training ; Variables ; Veterinary medicine and animal Health ; Weaning</subject><ispartof>PloS one, 2023-08, Vol.18 (8), p.e0289348-e0289348</ispartof><rights>Copyright: © 2023 Tırınk et al. 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For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. 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Hasan</au><au>Francois, Dominique</au><au>Marcon, Didier</au><au>Şen, Uğur</au><au>Shaikenova, Kymbat</au><au>Omarova, Karlygash</au><au>Tyasi, Thobela Louis</au><au>ALTAY, Yasin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-08-03</date><risdate>2023</risdate><volume>18</volume><issue>8</issue><spage>e0289348</spage><epage>e0289348</epage><pages>e0289348-e0289348</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. 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| subjects | Age Algorithms Analysis Animal biology Animals Artificial intelligence Biology and Life Sciences Birth weight Body weight Classification Computer and Information Sciences Computer Science Data mining Datasets Evaluation Females Genetic improvement Learning algorithms Life Sciences Machine learning Mars Modeling and Simulation People and Places Physical Sciences Physiological aspects Prediction models Regression analysis Research and Analysis Methods Sex Sheep Statistical methods Suckling behavior Support vector machines Training Variables Veterinary medicine and animal Health Weaning |
| title | Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed |
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