Comparison of logistic regression model and classification tree: An application to postpartum depression data

Ankaralı, Handan; Yazıcı, Ayşe Canan; Akkuş, Zeki; Buğdaycı, Resul; Sungur, Mehmet Alı

doi:10.1016/j.eswa.2006.02.022

Cited by 48 publications

(25 citation statements)

References 18 publications

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“…As a perfect alternative to logistic regression analysis, the tree-based CART algorithm detects significant explanatory variables affecting a dependent variable (Camdeviren et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

“…V-fold cross validation is accepted as 10 in the CART algorithm. Accuracy of classification rate, sensitivity, and specificity were calculated as defined by Camdeviren et al (2007).…”

Section: Methodsmentioning

confidence: 99%

“…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%

“…Classification and regression tree (CART) practiced for nominal, ordinal, and continuous variables is the data-mining algorithm used for constructing the decision tree as a good alternative to discriminant analysis and particularly logistic regression analysis (Camdeviren et al, 2007). The tree-based CART algorithm without finding any assumption necessary for explanatory variables is structured by a categorical dependent variable and both categorical and continuous explanatory variables for large data sets (Karabag et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

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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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“…As a perfect alternative to logistic regression analysis, the tree-based CART algorithm detects significant explanatory variables affecting a dependent variable (Camdeviren et al, 2007).…”

Section: Methodsmentioning

confidence: 99%

“…V-fold cross validation is accepted as 10 in the CART algorithm. Accuracy of classification rate, sensitivity, and specificity were calculated as defined by Camdeviren et al (2007).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2016

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“…Few works have been published on the comparison of classification techniques in different areas (Camdeviren, Yazici, Akkus, Bugdayci, & Sungur, 2007;Kurt, Ture, & Kurum, 2008).…”

Section: Introductionmentioning

confidence: 99%

Two-stage logistic regression model

Kim

2009

Expert Systems with Applications

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Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods

Çınaroğlu

2020

Intell Sys Acc Fin Mgmt

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Summary This study aims to compare the performances of logistic regression and random forest classifiers in a balanced oversampling procedure for the prediction of households that will face catastrophic out‐of‐pocket (OOP) health expenditure. Data were derived from the nationally representative household budget survey collected by the Turkish Statistical Institute for the year 2012. A total of 9,987 households returned valid surveys. The data set was highly imbalanced, and the percentage of households facing catastrophic OOP health expenditure was 0.14. Balanced oversampling was performed, and 30 artificial data sets were generated with sizes of 5% and 98% of the original data size. The balanced oversampled data set provided accurate predictions, and random forest exhibited superior performance in identifying households facing catastrophic OOP health expenditure (area under the receiver operating characteristic curve, AUC = 0.8765; classification accuracy, CA = 0.7936; sensitivity = 0.7765; specificity = 0.8552; F1 = 0.7797).

show abstract

Comparison of logistic regression model and classification tree: An application to postpartum depression data

Cited by 48 publications

References 18 publications

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

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

Two-stage logistic regression model

Modelling unbalanced catastrophic health expenditure data by using machine‐learning methods

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