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Data mining approach to policy analysis in a health insurance domain

Authors
 Young Moon Chae  ;  Seung Hee Ho  ;  Kyoung Won Cho  ;  Dong Ha Lee  ;  Sun Ha Ji 
Citation
 INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, Vol.62(2-3) : 103-111, 2001 
Journal Title
 INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS 
ISSN
 1386-5056 
Issue Date
2001
MeSH
Algorithms* ; Biometry ; Chi-Square Distribution ; Databases, Factual ; Decision Trees ; Female ; Health Promotion ; Humans ; Hypertension/prevention & control* ; Insurance, Health/statistics & numerical data* ; Korea ; Life Style ; Logistic Models ; Male ; Middle Aged ; Predictive Value of Tests ; Risk Factors ; Sensitivity and Specificity
Keywords
Knowledge management ; Data mining ; Logistic regression ; Hypertension ; Health insurance
Abstract
This study examined the characteristics of the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health outcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically, this study validated the predictive power of data mining algorithms by comparing the performance of logistic regression and two decision tree algorithms, CHIAD (Chi-squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) using the test set of 4588 beneficiaries and the training set of 13,689 beneficiaries. Contrary to the previous study, the CHIAD algorithm performed better than the logistic regression in predicting hypertension, and C5.0 had the lowest predictive power. In addition, the CHIAD algorithm and the association rule also provided the segment-specific information for the risk factors and target group that may be used in a policy analysis for hypertension management.
Full Text
http://www.sciencedirect.com/science/article/pii/S138650560100154X
DOI
10.1016/S1386-5056(01)00154-X
Appears in Collections:
4. Graduate School of Public Health (보건대학원) > Graduate School of Public Health (보건대학원) > 1. Journal Papers
Yonsei Authors
Jee, Sun Ha(지선하) ORCID logo https://orcid.org/0000-0001-9519-3068
Chae, Young Moon(채영문)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/143069
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