Detection of difficult calvings in dairy cows using boosted classification trees

Zaborski, Daniel; Grzesiak, Wilhelm; Kotarska, K.; Szatkowska, Iwona; Jedrzejczak, M

doi:10.5958/0976-0555.2014.00010.7

Cited by 9 publications

(22 citation statements)

References 15 publications

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“…Also, Sp and Acc (0.8186 to 0.8287 and 0.8111 to 0.8201, respectively) were approximately 7.0% lower compared with the values presented in the aforementioned study, in which LN were not investigated. In a similar research on dystocia detection in dairy heifers by means of boosted classification trees [ 10 ], Se and Acc on the training set were also higher (0.894 and 0.935, respectively) in comparison with the maximum values obtained in the present work, apart from Sp, which was 5.0% lower. However, in the case of GDA, Basarab et al [ 7 ] and Arthur et al [ 13 ] reported lower Se (0.222 to 0.471 and 0.255 to 0.400, respectively), with higher Sp (0.944 to 0.980 and 0.967 to 0.980, respectively) and Acc (0.852 to 0.917 and 0.846 to 0.885, respectively) investigating dystocia detection in beef heifers using linear discriminant function analysis.…”

Section: Discussionsupporting

confidence: 50%

“…E.g. Zaborski and Grzesiak [ 8 , 9 ] applied ANN to dystocia detection in Polish Holstein-Friesian Black-and-White cattle, Zaborski et al [ 10 ] used boosted classification trees for the same purpose, Morrison et al [ 11 , 12 ], Basarab et al [ 7 ], Arthur et al [ 13 ], and Johnson [ 14 ] applied linear discriminant function analysis for dystocia prediction in beef heifers, whereas Piwczyński et al [ 15 ] used decision trees for analyzing factors affecting dystocia in dairy cows.…”

Section: Introductionmentioning

confidence: 99%

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The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

Zaborski

Proskura

Grzesiak

2018

Asian-Australas J Anim Sci

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ObjectiveThe aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty.MethodsA total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable.ResultsThe maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam’s sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation.ConclusionThe potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

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Section: Discussionsupporting

confidence: 50%

Section: Introductionmentioning

confidence: 99%

The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

Zaborski

Proskura

Grzesiak

2018

Asian-Australas J Anim Sci

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“…9 0.71 (calculated from the same data used for training the models), somewhat higher than the average AUC for DT in our study (0.64), with the accuracy reported (61.5% correctly classified calving events) very similar to our DT model. Zaborski et al (2014a) compared assisted calving and UC events with boosted classification of our NN model performed on UC events (93%), but significantly higher than its performance on assisted calving and DC events (28% and 0%). When our models' predictions were reclassified to allow comparisons, they showed comparable performance.…”

Section: Discussionmentioning

confidence: 96%

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Fenlon¹,

O’Grady

Mee

et al. 2017

Journal of Dairy Science

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The aim of this study was to build and compare predictive models of calving difficulty in dairy heifers and cows for the purpose of decision support and simulation modeling. Models to predict 3 levels of calving difficulty (unassisted, slight assistance, and considerable or veterinary assistance) were created using 4 machine learning techniques: multinomial regression, decision trees, random forests, and neural networks. The data used were sourced from 2,076 calving records in 10 Irish dairy herds. In total, 19.9 and 5.9% of calving events required slight assistance and considerable or veterinary assistance, respectively. Variables related to parity, genetics, BCS, breed, previous calving, and reproductive events and the calf were included in the analysis. Based on a stepwise regression modeling process, the variables included in the models were the dam's direct and maternal calving difficulty predicted transmitting abilities (PTA), BCS at calving, parity; calving assistance or difficulty at the previous calving; proportion of Holstein breed; sire breed; sire direct calving difficulty PTA; twinning; and 2-way interactions between calving BCS and previous calving difficulty and the direct calving difficulty PTA of dam and sire. The models were built using bootstrapping procedures on 70% of the data set. The held-back 30% of the data was used to evaluate the predictive performance of the models in terms of discrimination and calibration. The decision tree and random forest models omitted the effect of twinning and included only subsets of sire breeds. Only multinomial regression and neural networks explicitly included the modeled interactions. Calving BCS, calving difficulty PTA, and previous calving assistance ranked as highly important variables for all 4 models. The area under the receiver operating characteristic curve (ranging from 0.64 to 0.79) indicates that all of the models had good overall discriminatory power. The neural network and multinomial regression models performed best, correctly classifying 75% of calving cases and showing superior calibration, with an average error in predicted probability of 3.7 and 4.5%, respectively. The neural network and multinomial regression models developed are both suitable for use in decision-support and simulation modeling.

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“…The algorithm produces binary nodes by dividing each node into two child nodes, recursively until homogeneous subgroups are obtained in the tree diagram. With the development of computer technology at the present time, statistical analysis of the algorithm is feasible in SAS (Statistical Analysis System), SPSS (Statistical Package for Social Sciences) (Ali et al, 2015), and STATISTICA (Camdeviren et al, 2007;Nisbet et In the classification problems, usage of various datamining algorithms for animal science has recently been recorded for beef cattle Kucukonder et al, 2015), dairy cattle (Grzesiak et al, 2010;Grzesiak et al, 2011;Zaborski et al, 2014;Bayram et al, 2015), and fisheries science (Topal et al, 2010). Yet, application of data-mining algorithms for poultry science has been scarce for Japanese quail Uckardes et al, 2014) and Chukar partridge (Alectoris chukar) (Karabag et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Usability of CART algorithm for determining egg quality characteristics influencing fertility in the eggs of Japanese quail

et al. 2016

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-The objective of this study was to determine the effects of egg quality characteristics (egg weight, egg width, egg height, and shape index) on fertility of eggs of Japanese quail with different colored feathers (yellow, white, grizzled, and normal), which are of economic importance for poultry production. For this purpose, 383 eggs of Japanese quail with various feather colors were used. In the study, usability of classification and regression tree (CART) data-mining algorithm as a classification tree method is necessary for poultry breeders to define proper cut-off values of egg quality characteristics that ensure Japanese quail eggs at good quality in fertility. Fertility as the dependent variable in the study was examined as a binary trait (fertile and infertile) and all the egg quality traits accepted as explanatory variables were continuous variables. Feather color was also included as a nominal categorical explanatory variable. The classification tree results showed that the highest fertility ratio of 90.9% was obtained from the eggs of Japanese quail of white, grizzled, and normal feather colors with 10.425 g ≥ egg weight, 24.565 mm ≥ egg width, or the fertility ratio of 88.2% of the eggs was observed with 10.425 g ≤ egg weight and 25.605 mm ≥ egg width. Consequently, usability of the tree-based CART algorithm is important in practice for properly establishing fertilized eggs, depending on feather color types of Japanese quail.

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Detection of difficult calvings in dairy cows using boosted classification trees

Cited by 9 publications

References 15 publications

The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

A comparison of 4 predictive models of calving assistance and difficulty in dairy heifers and cows

Usability of CART algorithm for determining egg quality characteristics influencing fertility in the eggs of Japanese quail

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