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基于机器学习的老年慢性心力衰竭病人衰弱风险预测模型的构建

海瑞 王慧 张蓉 徐亚萍 杨益

护理研究2024,Vol.38Issue(12):2103-2109,7.
护理研究2024,Vol.38Issue(12):2103-2109,7.DOI:10.12102/j.issn.1009-6493.2024.12.007

基于机器学习的老年慢性心力衰竭病人衰弱风险预测模型的构建

Construction of predictive model of frailty risk in elderly patients with chronic heart failure based on machine learning

海瑞 1王慧 1张蓉 1徐亚萍 1杨益2

作者信息

  • 1. 新疆医科大学护理学院,新疆 830011
  • 2. 新疆医科大学第一附属医院
  • 折叠

摘要

Abstract

Objective:To construct a predictive model of frailty risk in elderly patients with chronic heart failure(CHF)based on machine learning,and to provide a new method for accurate prediction of frailty occurrence in clinical elderly patients with CHF.Methods:Clinical data related to CHF patients from the cardiovascular medicine department of a tertiary grade A hospital in Urumqi from January 2023 to May 2023 were collected and randomly divided into training and testing sets in the ratio of 7∶3,with the occurrence of frailty as the outcome variable.The frailty risk prediction models were constructed based on four algorithms:Logistic regression(LR),decision tree(DT),random forest(RF),and support vector machines(SVM).The performance of the models was evaluated based on the area under curve(AUC),accuracy,precision,sensitivity,specificity,F1 value,and the optimal model was selected.Results:A total of 423 patients with CHF were included,182 of whom developed frailty(43%).All four prediction models had high accuracy,and the AUC values of the LR,DT,SVM,and RF models were 0.917,0.863,0.941 and 0.952,respectively,with the RF models having the highest AUC values,and the RF model had the highest accuracy,precision,sensitivity,specificity,and F1 value were the highest.The importance of the feature variables was further ranked based on the RF model,and the top five feature variables were hemoglobin,interleukin-6,albumin,malnutrition,and Charlson Comorbidity Index(CCI)scores.Conclusion:The predictive model of frailty risk in elderly patients with chronic heart failure based on RF machine learning has the best performance,which is helpful for early clinical assessment and prevention of frailty risk.

关键词

慢性心力衰竭/衰弱/机器学习/预测模型

Key words

chronic heart failure/frailty/machine learning/predictive model

引用本文复制引用

海瑞,王慧,张蓉,徐亚萍,杨益..基于机器学习的老年慢性心力衰竭病人衰弱风险预测模型的构建[J].护理研究,2024,38(12):2103-2109,7.

基金项目

新疆维吾尔自治区研究生创新项目,编号:XJ2021G230 ()

护理研究

OA北大核心CSTPCD

1009-6493

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