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 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.
data mining health insurance hypertension logistic regression knowledge management