Data mining approach to policy analysis in a health insurance domain

This study examined the characteristics of the knowledge discovery and data mining algorithms to demonstrate how they can be used to predict health ou tcomes and provide policy information for hypertension management using the Korea Medical Insurance Corporation database. Specifically, this study val idated the predictive power of data mining algorithms by comparing the perf ormance of logistic regression and two decision tree algorithms, CHIAD (Chi -squared Automatic Interaction Detection) and C5.0 (a variant of C4.5) usin g the test set of 4588 beneficiaries and the training set of 13,689 benefic iaries. Contrary to the previous study, the CHIAD algorithm performed bette r than the logistic regression in predicting hypertension, and C5.0 had the lowest predictive power. In addition, the CHIAD algorithm and the associat ion rule also provided the segment-specific information for the risk factor s and target group that may be used in a policy analysis for hypertension m anagement. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.