Multivariate data analysis and modeling through classification and regression trees
Computational Statistics & Data Analysis
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Integrating classification trees with local logistic regression in Intensive Care prognosis
Artificial Intelligence in Medicine
Comprehensible evaluation of prognostic factors and prediction of wound healing
Artificial Intelligence in Medicine
A statistical approach to growing a reliable honest tree
Computational Statistics & Data Analysis
Two-stage logistic regression model
Expert Systems with Applications: An International Journal
The study of a forecasting sales model for fresh food
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Comparing alternative classifiers for database marketing: The case of imbalanced datasets
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
In this study, it is aimed that comparing logistic regression model with classification tree method in determining social-demographic risk factors which have effected depression status of 1447 women in separate postpartum periods. In determination of risk factors, data obtained from prevalence study of postpartum depression were used. Cut-off value of postpartum depression scores that calculated was taken as 13. Social and demographic risk factors were brought up by helping of the classification tree and logistic regression model. According to optimal classification tree total of six risk factors were determined, but in logistic regression model 3 of their effect were found significantly. In addition, during the relations among risk factors in tree structure were being evaluated, in logistic regression model corrected main effects belong to risk factors were calculated. In spite of, classification success of maximal tree was found better than both optimal tree and logistic regression model, it is seen that using this tree structure in practice is very difficult. But we say that the logistic regression model and optimal tree had the lower sensitivity, possibly due to the fact that numbers of the individuals in both two groups were not equal and clinical risk factors were not considered in this study. Classification tree method gives more information with detail on diagnosis by evaluating a lot of risk factors together than logistic regression model. But making correct selection through constructed tree structures is very important to increase the success of results and to reach information which can provide appropriate explanations.