实用临床医药杂志2023，Vol.27Issue(22)：6-12,7.DOI:10.7619/jcmp.20232657
3种机器学习算法评估脑梗死患者颈动脉斑块稳定性的效能比较
Efficacy of three machine learning algorithms in evaluating stability of carotid plaque in patients with cerebral infarction
摘要
Abstract
Objective To explore the predictive efficacy of three machine learning algorithms for carotid plaque stability in patients with cerebral infarction.Methods The clinical data of 500 pa-tients with cerebral infarction were retrospectively analyzed.Univariate analysis and multivariate anal-ysis were used to determine the predictive factors entering the model.The prediction model of carotid plaque stability in patients with cerebral infarction was constructed based on nomogram,decision tree and random forest respectively.The enrolled patients were randomly divided into training set and test set according to the ratio of 7∶3.Sensitivity,specificity,accuracy,recall,accuracy and area under the curve(AUC)were used to compare the application efficiency of the model.Results The AUC of the nomogram model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.910(95％CI,0.950 to 0.983),the sensitivity was 0.910,the specificity was 0.917,the accuracy was 0.886,the recall rate was 0.910,and the accuracy rate was 0.914.The AUC of the decision tree model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.932(95％CI,0.903 to 0.961),the sensitivity was 0.903,the specificity was 0.922,the accuracy was 0.891,the recall rate was 0.903,and the accuracy rate was 0.914.The AUC of the random forest model for evaluating the stability of carotid plaque in patients with cerebral infarction in the training set was 0.984(95％CI,0.970 to 0.998),the sensitivity was 0.972,the specificity was 0.995,the accuracy was 0.993,the recall rate was 0.972,and the ac-curacy was 0.986.Conclusion The model based on the random forest algorithm has a better pre-diction effect and stability in evaluating the stability of carotid plaque in patients with cerebral infarc-tion,and its prediction efficiency is better than that of the Nomogram and decision tree.关键词
脑梗死/颈动脉斑块/稳定性/列线图/决策树/随机森林Key words
cerebral infarction/carotid plaque/stability/Nomogram/decision tree/random forest分类
医药卫生引用本文复制引用
湛先发,余小亚,王洪军,熊坤林..3种机器学习算法评估脑梗死患者颈动脉斑块稳定性的效能比较[J].实用临床医药杂志,2023,27(22):6-12,7.基金项目
重庆市科卫联合医学科研项目(2020MSXM044) （2020MSXM044）
