Risk stratification for LDL cholesterol using induction technique

Seung Hee Ho; Hyun Young Park; Yangsoo Jang; Sun Ha Jee

Risk stratification for LDL cholesterol using induction technique

Seung Hee Ho, Hyun Young Park, Yangsoo Jang, Sun Ha Jee

Department of Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics
Subtitle of host publication	Building Sustainable Health Systems
Pages	571-575
Number of pages	5
Volume	129
Publication status	Published - 2007 Dec 1
Event	12th World Congress on Medical Informatics, MEDINFO 2007 - Brisbane, QLD, Australia Duration: 2007 Aug 20 → 2007 Aug 24

Other

Other	12th World Congress on Medical Informatics, MEDINFO 2007
Country	Australia
City	Brisbane, QLD
Period	07/8/20 → 07/8/24

All Science Journal Classification (ASJC) codes

Biomedical Engineering
Health Informatics
Health Information Management

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@inproceedings{5cbbdf2df4174b98960e96ce218adc78,

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.",

author = "Ho, {Seung Hee} and Park, {Hyun Young} and Yangsoo Jang and Jee, {Sun Ha}",

year = "2007",

month = dec,

day = "1",

language = "English",

isbn = "9781586037741",

volume = "129",

pages = "571--575",

booktitle = "MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics",

note = "12th World Congress on Medical Informatics, MEDINFO 2007 ; Conference date: 20-08-2007 Through 24-08-2007",

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TY - GEN

T1 - Risk stratification for LDL cholesterol using induction technique

AU - Ho, Seung Hee

AU - Park, Hyun Young

AU - Jang, Yangsoo

AU - Jee, Sun Ha

PY - 2007/12/1

Y1 - 2007/12/1

N2 - 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.

AB - 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.

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UR - http://www.scopus.com/inward/citedby.url?scp=80052916002&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781586037741

VL - 129

SP - 571

EP - 575

BT - MEDINFO 2007 - Proceedings of the 12th World Congress on Health (Medical) Informatics

T2 - 12th World Congress on Medical Informatics, MEDINFO 2007

Y2 - 20 August 2007 through 24 August 2007

ER -