摘要
目的:应用Logistic回归、决策树CART和随机森林3种机器学习算法分别构建维持性血液透析病人衰弱风险预测模型,比较3种模型的预测效果。方法:选取2021年10月—2022年3月在杭州市2家三级甲等医院接受维持性血液透析治疗的病人485例,按照7∶3的比例随机分为训练集(n=341)和测试集(n=144),运用Logistic回归、决策树CART和随机森林建立维持性血液透析病人衰弱风险预测模型,采用准确率、灵敏度、特异度、阳性预测值、阴性预测值、Kappa系数和受试者工作特征(ROC)曲线下面积(AUC)对3种模型的预测性能进行比较。结果:训练集中,Logistic回归、决策树CART和随机森林的准确率分别为91.79%、91.50%、97.95%,特异度为96.84%、92.11%、96.91%,灵敏度为85.43%、90.73%、99.32%,阳性预测值为95.56%、90.13%、96.05%,阴性预测值为89.32%、92.59%、99.47%,Kappa值为0.832,0.828,0.958,AUC值为0.971,0.954,0.998。对3种模型的AUC值进行检验,结果发现随机森林模型与其余两种模型差异有统计学意义(P<0.05)。年龄、性别、查尔森合并疾病指数和营养风险筛查评分为3种预测模型的共同预测因子。结论:随机森林模型对维持性血液透析病人衰弱风险的预测性能优于Logistic回归和决策树CART。
Objective:To compare the value of risk assessment models based on Logistic regression,decision tree and random forest machine learning algorithms in predicting frailty risk among maintenance hemodialysis patients.Methods:From October 2021 to March 2022,a total of 485 patients receiving maintenance hemodialysis treatment in two tertiary grade-A hospitals in Hangzhou were selected and treated according to 7∶3 ratio was randomly divided into training set(n=341)and test set(n=144).Logistic regression,decision tree and random forest were used to establish frailty risk prediction models for maintenance hemodialysis patients.Accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Kappa and AUC value were used to compare the predictive performance of the three models.Results:In the training set,the accuracy of Logistic regression,CART,and random forest were 91.79%,91.50%and 97.95%,the specificity was 96.84%,92.11%,and 96.91%,and the sensitivity was 85.43%,90.73%,and 99.32%,respectively.The positive predictive value was 95.56%,90.13%,96.05%,the negative predictive value was 89.32%,92.59%,99.47%,the Kappa value was 0.832,0.828,0.958,and the AUC value was 0.971,0.954,0.998.The AUC values of the three models were tested,and the results showed that the random forest model was significantly different from the other two models(P<0.05).Age,gender,Charlson Comorbidity Index and nutritional risk screening score were common predictors of the three prediction models.Conclusion:Random forest model is the best model in predicting frailty risk among maintenance hemodialysis patients.
作者
汪丹丹
姚侃斐
祝雪花
WANG Dandan;YAO Kanfei;ZHU Xuehua(Department of Nursing,Zhejiang Chinese Medical University,Zhejiang 310053 China)
出处
《护理研究》
北大核心
2024年第1期8-16,共9页
Chinese Nursing Research
基金
浙江省卫生健康科技计划项目,编号:2022KY221。
