基于机器学习和logistic回归分析模型分析2型糖尿病轻度认知功能障碍的影响因素
Influential factors of mild cognitive impairment in type 2 diabetes mellitus based on machine learning and logistic regression
张红梅 1张宁 1孙玉娇 1张洲1
作者信息
- 1. 南京大学医学院附属鼓楼医院内分泌科,南京 210008
- 折叠
摘要
目的 应用机器学习和logistic回归分析模型分析2型糖尿病(type 2 diabetes mellitus,T2DM)轻度认知功能障碍的影响因素,为相关干预措施提供依据.方法 收集南京鼓楼医院内分泌科1 284例T2DM患者的资料,将数据分为训练集(70％)和测试集(30％),分别采用logistic回归分析、随机森林、XGBoost模型对数据建模并进行可解释性分析.结果 随机森林模型的预测性能最高,训练集的AUC值为1.0,测试集的AUC值为0.783(95％CI:0.660～0.894),模型筛选出T2DM患者发生轻度认知功能障碍的19个重要变量,如受教育时间、年龄、胰岛素敏感指数、周围神经病变、糖化血红蛋白、骨代谢异常等.结论 随机森林模型的预测性能最佳,可以协助医务人员准确识别T2DM患者发生轻度认知功能障碍的危险因素,有助于医务人员对患者进行早期预防.
Abstract
Objective To analyze the risk factors of mild cognitive impairment in type 2 diabetes mellitus(T2DM)by using machine learning and Logistic regression,and to provide basis for relevant intervention.Methods The data of 1 284 T2DM patients in the Department of Endocrinology of Nanjing Gulou Hospital were collected.The data were divided into the training set(70％)and the testing set(30％).Logistic regression model,Random Forest and XGBoost were used to construct the models,and the results were explained by the model interpretability.Results The random forest model had the highest predictive performance,the AUC value of the training set was 1.0 while the testing one was 0.783(95％CI:0.660-0.894),the model screened 19 important variables for MCI in patients with T2DM,such as education time,age,insulin sensitivity index,peripheral neuropathy,glycosylated hemoglobin,abnormal bone metabolism.Conclusions The prediction performance of random forest model is the best,which can help medical staff accurately identify the risk factors of MCI in patients with T2DM,and help medical staff to apply early intervention.
关键词
轻度认知功能障碍/影响因素/2型糖尿病/机器学习Key words
Mild cognitive impairment/Influencing factors/Type 2 diabetes mellitus/Machine learning引用本文复制引用
基金项目
国家自然科学基金(82000775)
南京大学中国医院改革发展研究院项目(NDYG2022078)
出版年
2024
NETL
NSTL
万方数据