Comparison of logistic regression model and classification tree: An application to postpartum depression data
Comparison of logistic regression model and classification tree: An application to postpartum depression data
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DOI：
10.1016/j.eswa.2006.02.022
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发表时间：
2007-05-01
影响因子：
8.5
通讯作者：
Sungur, Mehmet Ali
中科院分区：
文献类型：
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作者：
Camdeviren, Handan Ankarali;Yazici, Ayse Canan;Sungur, Mehmet Ali
本研究旨在比较logistic回归模型与分类树法，确定影响1447名妇女产后抑郁状态的社会人口危险因素。在确定危险因素时，采用了产后抑郁症患病率研究的数据。计算后的产后抑郁评分取截值13。通过分类树和logistic回归模型提出了社会和人口因素的危险因素。根据最优分类树共确定了6个危险因素，但在logistic回归模型3中，发现它们的影响显著。此外，在评价树状结构中各危险因素之间的关系时，在logistic回归模型中修正了属于危险因素的主效应。尽管极大树的分类成功率优于最优树和逻辑回归模型，但在实践中使用这种树结构是非常困难的。但我们认为logistic回归模型和最优树的敏感性较低，可能是由于两组的个体数量不相等，且本研究未考虑临床危险因素。与逻辑回归模型相比，分类树方法通过综合评价多种危险因素，提供了更多的诊断信息和细节。但是，通过构建的树形结构进行正确的选择，对于提高结果的成功率和获得能够提供适当解释的信息是非常重要的。(C) 2006 Elsevier Ltd版权所有。
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. (C) 2006 Elsevier Ltd. All rights reserved.