Objective To explore the factors influencing cognitive frailty in elderly inpatients with chronic heart failure(CHF)during hospitalisation,8 prediction models were developed with various machine learning algorithms to identify the best model as a guidance for medical staff on clinical interventions.Methods Convenience sampling method was used to select 650 elderly CHF inpatients who stayed in our hospital between September 2023 and June 2024 as the study objects in the cross-sectional investigation.A total of 607 patients had completed the study.The patients were divided into a cognitive frailty group and a non-cognitive frailty group according to the presence or absence of cognitive frailty.Variables were initially screened using univariate analysis and stepwise Logistic regression.The total sample was then randomly divided into a training set(n=424)and a testing set(n=183)of a 7:3 ratio.Eight predictive models were created using the algorithms of neural network(NN),k-nearest neighbour(KNN),linear discriminant analysis(LDA),support vector machine(SVM),naive Bayes(NB),logistic regression,decision tree(DT)and random forest(RF)on the training set.The predictive performance of the models was compared using the data of the testing set.Results The prevalence of cognitive frailty in elderly CHF inpatients was 48.3%.Results of Logistic regression showed that age,marital status,education,body mass index,multi-morbidity,nutritional status,medication,frequency of weekly exercise and the living conditions were the key factors(P<0.05).The overall accuracy in classification of the eight predictive models ranged from 0.803 to 0.847,with F1-values of 0.778 to 0.833,precision of 0.848 to 0.897,and recall rate of 0.700 to 0.778.The area under the receiver operating characteristic curve was 0.820 to 0.901.Conclusion Of the eight predictive models,the prediction model created with LDA shows the best performance and prediction in terms of comprehensive prediction metrics,while the prediction model created with NN shows the worst performance in comprehensive prediction.

Objective To explore the factors influencing cognitive frailty in elderly inpatients with chronic heart failure(CHF)during hospitalisation,8 prediction models were developed with various machine learning algorithms to identify the best model as a guidance for medical staff on clinical interventions.Methods Convenience sampling method was used to select 650 elderly CHF inpatients who stayed in our hospital between September 2023 and June 2024 as the study objects in the cross-sectional investigation.A total of 607 patients had completed the study.The patients were divided into a cognitive frailty group and a non-cognitive frailty group according to the presence or absence of cognitive frailty.Variables were initially screened using univariate analysis and stepwise Logistic regression.The total sample was then randomly divided into a training set(n=424)and a testing set(n=183)of a 7:3 ratio.Eight predictive models were created using the algorithms of neural network(NN),k-nearest neighbour(KNN),linear discriminant analysis(LDA),support vector machine(SVM),naive Bayes(NB),logistic regression,decision tree(DT)and random forest(RF)on the training set.The predictive performance of the models was compared using the data of the testing set.Results The prevalence of cognitive frailty in elderly CHF inpatients was 48.3%.Results of Logistic regression showed that age,marital status,education,body mass index,multi-morbidity,nutritional status,medication,frequency of weekly exercise and the living conditions were the key factors(P<0.05).The overall accuracy in classification of the eight predictive models ranged from 0.803 to 0.847,with F1-values of 0.778 to 0.833,precision of 0.848 to 0.897,and recall rate of 0.700 to 0.778.The area under the receiver operating characteristic curve was 0.820 to 0.901.Conclusion Of the eight predictive models,the prediction model created with LDA shows the best performance and prediction in terms of comprehensive prediction metrics,while the prediction model created with NN shows the worst performance in comprehensive prediction.