沈阳医学院学报2026，Vol.28Issue(1)：38-44,89,8.DOI:10.16753/j.cnki.1008-2344.2026.01.007
构建多模态机器学习驱动的老年脑卒中患者认知衰弱风险预测模型
Construction of a multimodal machine learning-based model for predicting the risk of cognitive frailty in elderly stroke patients
摘要
Abstract
Objective:To identify key influencing factors and construct a machine learning predictive model for cognitive frailty in elderly stroke patients,providing a basis for early clinical identification and intervention.Methods:This cross-sectional study(from Sep 2023 to Apr 2024)enrolled 420 elderly stroke patients from a tertiary hospital in Liaoning Province using convenience sampling.Cognitive frailty was assessed using the FRAIL Scale and Montreal Cognitive Assessment(MoCA),and participants were grouped accordingly.Data were collected via general information questionnaires,Barthel Index(BI),Geriatric Depression Scale(GDS-15),Mini Nutritional Assessment Short Form(MNA-SF),and Perceived Social Support Scale(PSSS).The subjects were divided into a training set and a validation set in a 7∶3 ratio.After variable selection via LASSO regression,five machine learning algorithms,including Logistic Regression and Random Forest,were employed to construct predictive models.Model performance was evaluated using receiver operating characteristic(ROC)curve and confusion matrices,with the optimal model interpreted using SHAP.Results:The prevalence of cognitive frailty among elderly stroke patients was 17.6%.The Support Vector Machine(SVM)model demonstrated the best performance,with an area under the ROC curve(AUC)of 0.978(95%CI:0.931-1.000)in the training cohort and 0.901(95%CI:0.807-0.995)in the validation cohort.SHAP analysis identified coronary heart disease,weekly exercise frequency,and age as the primary influencing factors.Conclusion:Cognitive frailty in elderly stroke patients is influenced by multiple factors,and the SVM prediction model can assist to identify high-risk individuals early and implement interventions.关键词
老年/脑卒中/认知衰弱/机器学习/风险预测模型Key words
the elderly/stroke/cognitive frailty/machine learning/risk prediction model分类
医药卫生引用本文复制引用
张会君,艾芳竹,李恩光,汪婷婷..构建多模态机器学习驱动的老年脑卒中患者认知衰弱风险预测模型[J].沈阳医学院学报,2026,28(1):38-44,89,8.基金项目
辽宁省社会科学规划课题项目(No.L21BGLO23) （No.L21BGLO23）
