中国现代医生2025，Vol.63Issue(15)：7-10,110,5.DOI:10.3969/j.issn.1673-9701.2025.15.002
基于机器学习构建高血压肾病预测模型研究
Construction of prediction models for hypertensive nephropathy based on machine learning
摘要
Abstract
Objective To explore the construction of a prediction model for hypertensive nephropathy(HN)based on machine learning.Methods A total of 318 hypertensive patients who visited Taizhou First People's Hospital from April 2023 to March 2024 were included and divided into a training set and a validation set at a ratio of 7:3.Least absolute shrinkage and selection operator(LASSO)algorithm was used to select clinical features from the training set,and 12 clinically significant variables were obtained from 18 clinical variables.Based on the Python 3.10 programming language,the training set was used to train the model.Taking the 12 clinically significant indicators were used as input variables,and whether the occurrence of HN was used as the outcome variable.Three machine learning algorithms,namely logistic regression,support vector machine,and artificial neural network,were used to construct prediction models.The test set was used for internal validation of three models.The performance of the models was compared through accuracy,area under the receiver operating characteristic curve,recall rate,precision,and F1.Results Among 12 clinically significant variables screened by the LASSO algorithm,cystatin C and urine protein qualitative were found to be the most predictive.The accuracy,area under the receiver operating characteristic curve,recall rate,precision,and F1 values of the Logistic regression,support vector machine,and artificial neural network prediction models constructed by machine learning was 0.94,0.96,0.95,0.87,0.91;0.94,0.97,0.96,0.86,0.91;0.91,0.94,0.93,0.80,0.86,respectively.Conclusion Logistic regression,support vector machine,and artificial neural network based on machine learning all have good predictive effects on the progression of hypertensive patients to HN.Among them,the predictive effects of Logistic regression and support vector machine are similar and better than artificial neural network prediction model.关键词
机器学习/高血压/高血压肾病/预测模型/逻辑回归/支持向量机/人工神经网络Key words
Machine learning/Hypertension/Hypertensive nephropathy/Prediction model/Logistic regression/Support vector machine/Artificial neural network分类
临床医学引用本文复制引用
柳明明,王黉,汪哲丞,陈丹..基于机器学习构建高血压肾病预测模型研究[J].中国现代医生,2025,63(15):7-10,110,5.基金项目
2021年度浙江省基础公益研究计划项目(LQ21H050001) （LQ21H050001）
2023年度浙江省中医药科技计划项目(2023ZF057) （2023ZF057）
