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3 种机器学习算法对维持性血液透析病人 衰弱风险预测性能比较.

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 maintena...

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Publicado en:Chinese Nursing Research Vol. 38; no. 1; pp. 8 - 17
Autores principales: 汪丹丹, 姚侃斐, 祝雪花
Formato: research tables/charts Journal Article
Publicado: Chinese Nursing Research Editorial Office Jan2024
Acceso en línea:Ver este registro en EBSCOhost
Sumario: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.
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.