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基于逻辑回归和决策树模型的中风患者耻辱感预测因素

Predictive factors of stigma in stroke patients based on logistic regression and decision tree mode.

作者信息

Ma Wenwen, Jing Kunjuan, Zhang Ruotong, Li Xuefei, Li Zheng

机构信息

Wenwen Ma, Ear, Nose and Throat Department, School of Nursing, Hebei University, Baoding, Hebei, China. Baoding No.1 Central Hospital, Baoding, Hebei, China.

Kunjuan Jing, School of Nursing, Hebei University, Baoding, Hebei, China.

出版信息

Pak J Med Sci. 2025 May;41(5):1482-1487. doi: 10.12669/pjms.41.5.9946.

DOI:10.12669/pjms.41.5.9946
PMID:40469131
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12130943/
Abstract

OBJECTIVE

Logistic regression and decision tree model were used to analyze the predictive factors of stigma in stroke patients, and to explore the application value of the two models.

METHODS

This was a retrospective study. The data of 342 stroke patients were collected from Baoding No.1 Central Hospital from December 2023 to March 2024. Data were retrospectively retrieved from the hospital information and management system. The regression model and decision tree model of influencing factors of stroke patients' sense of stigma were established, to analyze the influencing factors of the sense of stigma, and to compare the predictive effects, advantages and disadvantages of the two models.

RESULTS

Logistic regression analysis showed that threat assessment (OR=2.7761) was a risk factor for stigma, while irrelevant cognitive appraisal (OR=0.321), social support (OR=0.098) and resilience (OR=0.438) were protective factors. The results of the decision tree model showed that the patients' psychological resilience was the most important factor affecting the sense of stigma, followed by social support and threat assessment. The AUC of the decision tree model and Logistic regression model were 0.854 and 0.880, respectively, and the accuracy were 78.7% and 79.6%, respectively.

CONCLUSION

Threat, irrelevant cognitive appraisal, social support and resilience might be the predictive factors of stigma in stroke patients. The AUC and accuracy of the decision tree model were slightly lower than that of the Logistic regression model.

摘要

目的

采用逻辑回归和决策树模型分析脑卒中患者耻辱感的预测因素,并探讨两种模型的应用价值。

方法

本研究为回顾性研究。收集2023年12月至2024年3月保定市第一中心医院342例脑卒中患者的数据。数据从医院信息管理系统中回顾性获取。建立脑卒中患者耻辱感影响因素的回归模型和决策树模型,分析耻辱感的影响因素,并比较两种模型的预测效果、优缺点。

结果

逻辑回归分析显示,威胁评估(OR=2.7761)是耻辱感的危险因素,而无关认知评价(OR=0.321)、社会支持(OR=0.098)和心理韧性(OR=0.438)是保护因素。决策树模型结果显示,患者的心理韧性是影响耻辱感的最重要因素,其次是社会支持和威胁评估。决策树模型和逻辑回归模型的AUC分别为0.854和0.880,准确率分别为78.7%和79.6%。

结论

威胁、无关认知评价、社会支持和心理韧性可能是脑卒中患者耻辱感的预测因素。决策树模型的AUC和准确率略低于逻辑回归模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d368/12130943/2f2a615dec2b/PJMS-41-1482-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d368/12130943/2f2a615dec2b/PJMS-41-1482-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d368/12130943/2f2a615dec2b/PJMS-41-1482-g001.jpg

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