基于Logistic 回归模型与决策树模型分析缺血性脑卒中后抑郁影响因素

打开文本图片集

Influencing factors analysis of post⁃stroke depression in patients with ischemic stroke based on Logistic regression model and decision tree model

ZHANG Xinyue， SHA Kaihui， SONG Shuxia， WANG Fang， CHENG Meiling

School of Nursing， Binzhou Medical University， Shandong 256600 China

Corresponding Author  SHA Kaihui， E⁃mail： skhui328175@163.com

Abstract  Objective：To investigate the status quo of post⁃stroke depression（PSD） in patients with ischemic stroke， and to explore its influencing factors by Logistic regression model and decision tree model.Methods：A total of 215 patients who were diagnosed with ischemic stroke in a tertiary grade A hospital in Binzhou city were selected as subjects by convenience sampling method from January to October 2022.Logistic regression model and decision tree C5.0 model were constructed for analyzing the influencing factors of PSD.The predictive performance of two models was compared.Results：A total of 215 patients with ischemic stroke in the study were investigated.The PSD incidence rate was 37.7%.Logistic regression model showed that sex，lesion location，Barthel Index（BI） score，homocysteine and red blood cell distribution width （CV） were risk factors for PSD.The area under the curve（AUC） of receiver operator characteristic of the model was 0.772.Decision tree C5.0 model showed that BI score，lesion location and diabetes were influencing factors of PSD.The AUC of the  model was 0.729.There was no significant difference in the prediction efficiency between the two models （Z=1.737，P=0.082）.Conclusions：The incidence of PSD in patients with ischemic stroke is relatively high，and Logistic regression model and decision tree C5.0  model have  certain predictive value for it.The two models should be combined to improve prediction performance and achieve early identification and intervention of PSD.

Keywords    Logistic regression； decision tree； ischemic stroke； post⁃stroke depression； influencing factors； nursing

摘要  目的：了解缺血性脑卒中病人卒中后抑郁（PSD）发生现状，采用Logistic回归模型和决策树C5.0模型探讨其影响因素。（剩余12361字）