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

作　　者：海瑞 王慧 张蓉 徐亚萍 杨益[2] HAI Rui;WANG Hui;ZHANG Rong;XU Yaping;YANG Yi(School of Nursing,Xinjiang Medical University,Xinjiang 830011 China)

机构地区：[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

出　　处：《护理研究》2024年第12期2103-2109,共7页Chinese Nursing Research

基　　金：新疆维吾尔自治区研究生创新项目,编号:XJ2021G230。

摘　　要：目的:基于机器学习算法构建老年慢性心力衰竭(CHF)病人衰弱风险预测模型,为临床老年CHF病人衰弱发生的精准预测提供新方法。方法:收集2023年1月—5月乌鲁木齐市某三级甲等医院心血管内科的CHF病人相关临床资料,按7∶3比例随机划分为训练集和测试集,以是否发生衰弱为结局变量,分别基于逻辑回归(LR)、决策树(DT)、随机森林(RF)、支持向量机(SVM)4种机器学习算法构建衰弱风险预测模型。基于受试者工作特征曲线下面积(AUC)、准确度、精确度、灵敏度、特异度、F1分数评估模型性能,选出最优模型。结果:共纳入423例CHF病人,其中182例病人发生衰弱,衰弱发生率为43%。4种预测模型都有较高的准确性,LR、DT、SVM、RF模型的AUC值分别为0.917,0.863,0.941,0.952,其中RF模型AUC值、准确度、精确度、灵敏度、特异度、F1分数均最高。进一步基于RF模型对特征变量进行重要性排序,其中排名前5位的特征变量依次为血红蛋白、白细胞介素⁃6、白蛋白、营养不良、查尔森合并症指数(CCI)。结论:基于RF机器学习算法构建的老年CHF病人衰弱风险预测模型性能最优,有助于临床早期评估和预防其衰弱风险的发生。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 

分 类 号：R473.5[医药卫生—护理学]