Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes
We used data mining techniques to build predictive models to identify very high-cost patients. A refined Medical Expenditure Panel Survey dataset of 31,704 records was modeled. CHAID was the top performing predictive model. Created a set of 5 non-trivial attributes to identify the top 5% of high cost patients. The results of this study can improve the delivery of health services.
data mining predictive models medical expenditure panel survey healthcare expenditures decision tree