Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes  

作　　者：Chung-Chi Yang Chung-Hsin Peng Li-Ying Huang Fang Yu Chen Chun-Heng Kuo Chung-Ze Wu Te-Lin Hsia Chung-Yu Lin 

机构地区：[1]Division of Cardiovascular Medicine,Taoyuan Armed Forces General Hospital,Taoyuan City 32551,Taiwan [2]Division of Cardiovascular,Tri-service General Hospital,Taipei City 114202,Taiwan,China [3]Department of Urology,Cardinal Tien Hospital,New Taipei City 23148,Taiwan,China [4]School of Medicine,Fu-Jen Catholic University,New Taipei City 242062,Taiwan,China [5]Department of Internal Medicine,Department of Medical Education,School of Medicine,Fu Jen Catholic University Hospital,New Taipei City 243,Taiwan,China [6]School of Medicine,College of Medicine,Fu Jen Catholic University,New Taipei City 243,Taiwan,China [7]Department of Endocrinology,Fu Jen Catholic University Hospital,New Taipei City 243,Taiwan,China [8]Division of Endocrinology and Metabolism,Department of Internal Medicine,Fu Jen Catholic University Hospital,New Taipei City 243,Taiwan,China [9]Division of Endocrinology,Shuang Ho Hospital,New Taipei City 23561,Taiwan,China [10]School of Medicine,Taipei Medical University,Taipei City 11031,Taiwan,China [11]Department of Internal Medicine,Cardinal Tien Hospital,New Taipei City 23148,Taiwan,China [12]Department of Cardiology,Fu Jen Catholic University Hospital,New Taipei City 24352,Taiwan,China [13]Graduate Institute of Business Administration,Fu Jen Catholic University,New Taipei City 242062,Taiwan,China

出　　处：《World Journal of Clinical Cases》2023年第33期7951-7964,共14页世界临床病例杂志

基　　金：The study was reviewed and approved by the Cardinal Tien Hospital Institutional Review Board(Approval No.CTH-102-2-5-024).

摘　　要：BACKGROUND The prevalence of type 2 diabetes(T2D)has been increasing dramatically in recent decades,and 47.5%of T2D patients will die of cardiovascular disease.Thallium-201 myocardial perfusion scan(MPS)is a precise and noninvasive method to detect coronary artery disease(CAD).Most previous studies used traditional logistic regression(LGR)to evaluate the risks for abnormal CAD.Rapidly developing machine learning(Mach-L)techniques could potentially outperform LGR in capturing non-linear relationships.AIM To aims were:(1)Compare the accuracy of Mach-L methods and LGR;and(2)Found the most important factors for abnormal TMPS.METHODS 556 T2D were enrolled in the study(287 men and 269 women).Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable.Subjects with a MPS score≥9 were defined as abnormal.In addition to traditional LGR,classification and regression tree(CART),random forest,Naïve Bayes,and eXtreme gradient boosting were also applied.Sensitivity,specificity,accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.RESULTS Except for CART,the other Mach-L methods outperformed LGR,with gender,body mass index,age,low-density lipoprotein cholesterol,glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.CONCLUSION Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D,with the most important risk factors being gender,body mass index,age,low-density lipoprotein cholesterol,glycated hemoglobin and smoking.

关 键 词：Myocardial perfusion scintigraphy Machine learning Type 2 diabetes Thallium-201 

分 类 号：R587.2[医药卫生—内分泌]