Application of machine-learning algorithms to predict calving difficulty in Holstein dairy cattle
نویسندگان
چکیده
Context An ability to predict calving difficulty could help farmers make better farm-management decisions, thereby improving dairy farm profitability and welfare.Aims This study aimed in Iranian herds using machine-learning (ML) algorithms evaluate sampling methods deal with imbalanced datasets.Methods For this purpose, the history records of cows that calved between 2011 2021 on two commercial farms were used. Using WEKA software, four commonly used ML algorithms, namely naïve Bayes, random forest, decision trees, logistic regression, applied dataset. The was considered as a binary trait 0, normal or unassisted calving, 1, difficult i.e. receiving any during parturition from personnel involvement surgical intervention. average rate 18.7%, representing an Therefore, down-sampling cost-sensitive techniques implemented tackle problem. Different models evaluated basis F-measure area under curve.Key results showed improved predictive model (P=0.07, P=0.03, for respectively). ranged 0.387 (decision tree) 0.426 (logistic regression) balanced However, when original dataset, Bayes had best performance up 0.388 terms F-measure.Conclusions Overall, prediction compared Although performed worse than expected (due missing values), implementation can still lead effective method predicting difficulty.Implications research indicated capability incidence within but more explanatory variables (e.g. genetic information) are required improve based unbalanced
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ژورنال
عنوان ژورنال: Animal Production Science
سال: 2023
ISSN: ['1836-5787', '1836-0939']
DOI: https://doi.org/10.1071/an22461