title: Risk stratification for LDL cholesterol using induction technique.
abstract: 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.
authors.affiliation: Department of Health Informatics, Graduate School of Public Health, Yonsei University, Seoul, Korea.
authors.forename: Seung Hee
authors.initials: SH
authors.lastname: Ho
authors.forename: Hyun Young
authors.initials: HY
authors.lastname: Park
authors.forename: Yang Soo
authors.initials: YS
authors.lastname: Jang
authors.forename: Sun Ha
authors.initials: SH
authors.lastname: Jee
mesh_terms: 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
chemical_list: D008078:Cholesterol, LDL