A comparative study of risk prediction models for atherosclerosis in patients with diabetes mellitus
HAN Zhengyuan
YANG Ziwei
ZHAO Luyang
LI Linyi
LIU Kui
WAN Yi
Abstract：Objective To analyze and compare the application of LightGBM and random forest machine learning model in the risk prediction model of atherosclerosis in diabetic patients.Methods Based on the public data set of National Population Health Data Center,the risk prediction model of atherosclerosis was established and compared with LightGBM and random forest algorithms.Results The machine learning model of LightGBM and random forest was used to analyze atherosclerosis.It was found that the accuracy of random forest was 0.624 2,area under the curve(AUC)was 0.671 8,and precision was 0.629 7,which were all higher than those of LightGBM.However,the recall rate of LightGBM and F1 score of LightGBM were 0.756 7 and 0.665 2,which were higher than those of random forest,but both of them had good prediction effects for atherosclerosis.Conclusion In the prediction model of atherosclerosis,random forest has a higher accuracy,AUC and precision,while LightGBM has a higher recall rate and F1 score.In general,both of them can accurately predict atherosclerosis,which can be applied to clinical practice and provide useful reference for the related research of clinical auxiliary diagnosis of diabetic complications.
Keywords：diabetes mellitusatherosclerosismachine learningprediction analysis
Publication Date：2024-08-28
Online Publishing Date：2026-08-26(First online date of this platform, not the publication date of the document)
Pages：5( 930-934 )