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

Abstract Background Hypertension is a common chronic disease in the world, and it is also a common basic disease of cardiovascular and brain complications. Overweight and obesity are the high risk factors of hypertension. In this study, three statistical methods, classification tree model, logistic regression model and BP neural network, were used to screen the risk factors of hypertension in overweight and obese population, and the interaction of risk factors was conducted Analysis, for the early detection of hypertension, early diagnosis and treatment, reduce the risk of hypertension complications, have a certain clinical significance.Methods The classification tree model, logistic regression model and BP neural network model were used to screen the risk factors of hypertension in overweight and obese people.The specificity, sensitivity and accuracy of the three models were evaluated by receiver operating characteristic curve (ROC). Finally, the classification tree CRT model was used to screen the related risk factors of overweight and obesity hypertension, and the non conditional logistic regression multiplication model was used to quantitatively analyze the interaction.Results The Youden index of ROC curve of classification tree model, logistic regression model and BP neural network model were 39.20%,37.02% ,34.85%, the sensitivity was 61.63%, 76.59%, 82.85%, the specificity was 77.58%, 60.44%, 52.00%, and the area under curve (AUC) was 0.721, 0.734,0.733, respectively. There was no significant difference in AUC between the three models (P>0.05). Classification tree CRT model and logistic regression multiplication model suggested that the interaction between NAFLD and FPG was closely related to the prevalence of overweight and obese hypertension.Conclusion NAFLD,FPG,age,TG,UA, LDL-C were the risk factors of hypertension in overweight and obese people. The interaction between NAFLD and FPG increased the risk of hypertension.

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A STUDY ON PREVALENCE OF POSTPARTUM DEPRESSION AT TERTIARY HOSPITAL, WEST BENGAL.

10.36106/ijar/8608857 ◽

2021 ◽

pp. 33-36

Author(s):

Chandrima Maity ◽

Debasish Sanyal ◽

Arati Biswas ◽

Sudarsan Saha

Keyword(s):

Logistic Regression ◽

Postpartum Depression ◽

Regression Model ◽

Logistic Regression Model ◽

Depression Scale ◽

Sampling Technique ◽

Simple Random Sampling ◽

Background Information ◽

Stepwise Logistic Regression ◽

Postpartum Mothers

The investigators assessed the prevalence of Postpartum Depression (PPD), its clinical features and relationship of PPD with socio-demographical and obstetrical factors. The samples were selected from the OPD and IPD, of a Medical college in Kolkata.. Observational study was performed on 500(N=500) postpartum mothers who were selected by using Simple Random Sampling Technique within the six weeks of postpartum period. Data were collected by using the Structured Questionnaire for background information, Edinburgh Postnatal Depression Scale (Bengali Version of EPDS) for postpartum depression. Data analysis was performed using Descriptive Statistics, Chi-square, Logistic Regression and Decision Tree. A total of 112 (Prevalence Rate 22.4%) postpartum mothers had PPD. Stepwise logistic regression model correctly classied 92.2% of women who developed PPD. Using logistic regression model, postpartum depression is best predicted by: No. of Postpartum days p< 0.001***, Age of the mother p<0.024**, Religion p<0.003**, Type of family p<0.020**, Education of the mother p<0.001***, Monthly Income of the family p<0.001***, No of other living children p<0.001***, Pregnancy outcome p<0.033**, Any complication during pregnancy / delivery/ postpartum p< 0.001*** and Problems with family members p< 0.001***. The study recommends that evaluation should be carried out for Postpartum Depression and its risk factors to prevent and treat PPD in a timely manner.

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Differentiation of Benign and Malignant Breast Tumors by Logistic Regression and a Classification Tree using Doppler flow signals

Methods of Information in Medicine ◽

10.1055/s-0038-1634530 ◽

1998 ◽

Vol 37 (03) ◽

pp. 226-234 ◽

Cited By ~ 18

Author(s):

H. Madjar ◽

H. J. Prömpeler ◽

W. Sauerbrei

Keyword(s):

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Malignant Tumors ◽

Classification Tree ◽

Color Doppler ◽

Error Rates ◽

Discriminative Ability ◽

Malignant Breast ◽

Highly Correlated

AbstractIn breast examinations with Doppler, an increased flow is found in malignant tumors. With the relatively new color Doppler, we measured different flow values in 133 cancer patients and in 325 women with benign disease. These measurements were used to develop diagnostic rules. For the highly correlated flow values, we used a stepwise procedure to select a final logistic regression model and a tree-based approach, which is a different way of modeling. With both approaches we developed simple diagnostic rules of which the sensitivity and the specificity exceeded 90%. There are no differences between the two approaches concerning discriminative ability. As complex statistical modeling leads to an overoptimism in the assessment of the error rates, we applied sensitivity analysis, investigated the stability of the selected logistic regression model, and estimated the magnitude of the overoptimism of the diagnostic rules with resampling methods. The results indicate that the estimates of sensitivity and specificity are probably close to realistic values for a clinical setting.

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Risk factors affecting crash injury severity for different groups of e-bike riders: A classification tree-based logistic regression model

Journal of Safety Research ◽

10.1016/j.jsr.2020.12.009 ◽

2020 ◽

Author(s):

Zhengwu Wang ◽

Shuai Huang ◽

Jie Wang ◽

Denisa Sulaj ◽

Wei Hao ◽

...

Keyword(s):

Risk Factors ◽

Logistic Regression ◽

Regression Model ◽

Logistic Regression Model ◽

Injury Severity ◽

Classification Tree ◽

Factors Affecting ◽

Crash Injury Severity

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Statistical Process Control for Monitoring Standardized Mortality Ratios of a Classification Tree Model

Methods of Information in Medicine ◽

10.3414/me11-02-0044 ◽

2012 ◽

Vol 51 (04) ◽

pp. 353-358 ◽

Cited By ~ 5

Author(s):

S. Eslami ◽

N. de Keizer ◽

E. de Jonge ◽

S. E. de Rooij ◽

A. Abu-Hanna ◽

...

Keyword(s):

Logistic Regression ◽

Quality Of Care ◽

Regression Model ◽

Logistic Regression Model ◽

Classification Tree ◽

Brier Score ◽

Tree Model ◽

Care Assessment ◽

Over Time

SummaryObjectives: The ratio of observed to expected mortality (standardized mortality ratio, SMR), is a key indicator of quality of care. We use PreControl Charts to investigate SMR behavior over time of an existing tree-model for predicting mortality in intensive care units (ICUs) and its implications for hospital ranking. We compare the results to those of a logistic regression model.Methods: We calculated SMRs of 30 equally-sized consecutive subsets from a total of 12,143 ICU patients aged 80 years or older and plotted them on a PreControl Chart. We calculated individual hospital SMRs in 2009, with and without repeated recalibration of the models on earlier data.Results: The overall SMR of the tree-model was stable over time, in contrast to logistic regression. Both models were stable after repeated recalibration. The overall SMR of the tree on the whole validation set was statistically significantly different (SMR 1.00 ± 0.012 vs. 0.94 ± 0.01) and worse in performance than the logistic regression model (AUC 0.76 ± 0.005 vs. 0.79 ± 0.004; Brier score 0.17 ± 0.012 vs. 0.16 ± 0.010). The individual SMRs’ range in 2009 was 0.53–1.31 for the tree and 0.64–1.27 for logistic regression. The proportion of individual hospitals with SMR >1, hinting at poor quality of care, reduced from 38% to 29% after recalibration for the tree, and increased from 15% to 35% for logistic regression.Conclusions: Although the tree-model has seemingly a longer shelf life than the logistic regression model, its SMR may be less useful for quality of care assessment as it insufficiently responds to changes in the population over time.

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