Application of classification trees and logistic regression to determine factors responsible for lamb mortality

Piwczyński, Dariusz; Sitkowska, Beata; Wiśniewska, Ewa

doi:10.1016/j.smallrumres.2011.09.014

Cited by 16 publications

(16 citation statements)

References 18 publications

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“…The ROC index values (0.656-0.675) obtained mean that the forecasting capability of all models is moderate. A similar assessment of the value of this index was obtained in the area of sheep farming through earlier studies on lamb collapse (Piwczyński et al 2011) with the use of classification trees (0.645-0.648) and a logistic regression (0.609-0.634). Austin (2007), however, cites higher values for the ROC index, when modelling mortality among patients, caused by acute myocardial infarction (0.779-0.849).…”

Section: Models Comparisonmentioning

confidence: 59%

“…the χ 2 test, a logistic regression (Nash et al 1996, Binns et al 2002, Piwczyński 2009. According to recent studies (Piwczyński et al 2011), a possible alternative method which can be used is the classification tree technique, which belongs to the analysis representation area known as data mining. An analysis of clusters and artificial neuron networks are distinguished in this area of statistics (SAS Institute Inc. 2009).…”

Section: Introductionmentioning

confidence: 99%

“…The classification tree technique has already been used in human medicine (Austin 2007). Its suitability in the field of sheep farming for modelling lamb mortality has been proved (Piwczyński et al 2011). One very significant advantage of using the tree structure is an easy interpretation of data through the graphic representation of a model depicting very complex effects arising from various factors and the interplay within those factors, by which high-order interactions may take place, influencing visual traits measured on the nominal and ordinal scale of the tree presentation.…”

Section: Introductionmentioning

confidence: 99%

“…Owing to the classification tree technique, it is viable to indicate clearly the best and worst solutions available, i.e. present a set of these levels of individual experimental factors that guarantee the lowest and highest level of a trait (Piwczyński et al 2011).…”

Section: Introductionmentioning

confidence: 99%

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Statistical modelling of somatic cell counts using the classification tree technique

Piwczyński

Sitkowska

2012

Arch. Anim. Breed.

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Abstract. The research studied a sample of 455 Polish Holstein-Friesian Black and White cows. Its aim was to apply and compare two modern statistical methods, i.e. classification trees and a logistic regression in examination of the impact of selected lactation-related factors (successive lactation, herd size and production level, year of calving, calving season, test day season, lactation phases and the amount of milk obtained in a test milking) on the somatic cell counts. Two different division criteria were taken into account in the creation of classification trees, i.e. entropy reduction and Gini coefficient. The quality of classification trees and multiple regression models was compared taking into consideration the following criteria: an average squared error, cumulative lift, Kolmogorov-Smirnov statistics and the area under the ROC curve. Having conducted the research, it may be concluded that from among the statistical methods applied, the best modelling of the level of somatic cell counts was obtained using the classification tree technique when the division criterion was based on the entropy function. According to the results of the study, somatic cell counts were diversified by the following factors, in a decreasing order of importance: herd production level, year of calving, subsequent lactation, calving season, day of test milking, herd size and the month used to take milk samples. Using somatic cell count as an udder health benchmark, it may be concluded that cows requiring particular attention as a result of udder diagnosis are from those in herds with high milk production levels, with individual cows producing up to 15 kg of milk.

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Section: Models Comparisonmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Statistical modelling of somatic cell counts using the classification tree technique

Piwczyński

Sitkowska

2012

Arch. Anim. Breed.

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“…One of the approaches to the identifi cation of cows with an increased risk of developing mastitis during their life-span is based on the use of statistical methods for classifi cation tasks and, more specifi cally, data mining techniques, which have become increasingly popular in the recent years in various animal farming applications [22][23][24][25]. For example, decision trees are tree-like representations of a learned function used for approximating a target variable (such as health status) with the discrete values from a given dataset and consist of nodes connected with branches, of which terminal nodes (leaves) contain the target values [26].…”

Section: Introductionmentioning

confidence: 99%

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

et al. 2016

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The aim of the present study was to: 1) check whether it would be possible to detect cows susceptible to mastitis at an early stage of their utilization based on selected genotypes and basic production traits in the fi rst three lactations using ensemble data mining methods (boosted classifi cation tress -BT and random forest -RF), 2) fi nd out whether the inclusion of additional production variables for subsequent lactations will improve detection performance of the models, 3) identify the most signifi cant predictors of susceptibility to mastitis, and 4) compare the results obtained by using BT and RF with those for the more traditional generalized linear model (GLZ). A total of 801 records for Polish Holstein-Friesian Black-and-White cows were analyzed. The maximum sensitivity, specifi city and accuracy of the test set were 72.13%, 39.73%, 55.90% (BT), 86.89%, 17.81%, 59.49% (RF) and 90.16%, 8.22%, 58.97% (GLZ), respectively. Inclusion of additional variables did not have a signifi cant effect on the model performance. The most signifi cant predictors of susceptibility to mastitis were: milk yield, days in milk, sire's rank, percentage of Holstein-Friesian genes, whereas calving season and genotypes (lactoferrin, tumor necrosis factor alpha, lysozyme and defensins) were ranked much lower. The applied models (both data mining ones and GLZ) showed low accuracy in detecting cows susceptible to mastitis and therefore some other more discriminating predictors should be used in future research.

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Developing a Bayesian Model of Climate-Induced Lake Overturn in Talisay, Taal Lake

Magcale-Macandog

Salvacion

Pabico

et al. 2023

Ecosystem and Species Habitat Modeling for Conservation and Restoration

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Application of classification trees and logistic regression to determine factors responsible for lamb mortality

Cited by 16 publications

References 18 publications

Statistical modelling of somatic cell counts using the classification tree technique

Statistical modelling of somatic cell counts using the classification tree technique

Identification of Cows Susceptible to Mastitis based on Selected Genotypes by Using Decision Trees and A Generalized Linear Model

Developing a Bayesian Model of Climate-Induced Lake Overturn in Talisay, Taal Lake

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