Risk stratification for LDL cholesterol using induction technique.
We identified the combined patterns of LDL cholesterol risk factors including biometric, environmental and genetic factors using induction technique. In this hospital based cardiovascular genome study of Korean men and women, we found that CART (classification and regression tree) was a better method to predict LDL cholesterol compared to the regression method. The CART had a better prediction ability than the multiple regression for male and female, respectively. We also identified combined patterns of LDL cholesterol risk factors and segment specific information for LDL cholesterol management using induction rules. The CART method provided more detailed results according to each segmentation and subgroup. In addition, we demonstrated how the CART algorithm could be used in risk assessment and target segmentation of LDL cholesterol management.
Ho
Seung Hee
SH
Department of Health Informatics, Graduate School of Public Health, Yonsei University, Seoul, Korea.
Park
Hyun Young
HY
Jang
Yang Soo
YS
Jee
Sun Ha
SH
D000465:Algorithms; D001699:Biometry; D002318:Cardiovascular Diseases / Q000235:genetics; D008078:Cholesterol, LDL / Q000097:blood*; D003663:Decision Trees; D005260:Female; D006801:Humans; D006937:Hypercholesterolemia / Q000209:etiology / Q000235:genetics; D008297:Male; D011110:Polymorphism, Genetic; D012044:Regression Analysis; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
D008078:Cholesterol, LDL