Comparison of hospital charge prediction models for colorectal cancer patients: neural network vs. decision tree models.
Analysis and prediction of the care charges related to colorectal cancer in Korea are important for the allocation of medical resources and the establishment of medical policies because the incidence and the hospital charges for colorectal cancer are rapidly increasing. But the previous studies based on statistical analysis to predict the hospital charges for patients did not show satisfactory results. Recently, data mining emerges as a new technique to extract knowledge from the huge and diverse medical data. Thus, we built models using data mining techniques to predict hospital charge for the patients. A total of 1,022 admission records with 154 variables of 492 patients were used to build prediction models who had been treated from 1999 to 2002 in the Kyung Hee University Hospital. We built an artificial neural network (ANN) model and a classification and regression tree (CART) model, and compared their prediction accuracy. Linear correlation coefficients were high in both models and the mean absolute errors were similar. But ANN models showed a better linear correlation than CART model (0.813 vs. 0.713 for the hospital charge paid by insurance and 0.746 vs. 0.720 for the hospital charge paid by patients). We suggest that ANN model has a better performance to predict charges of colorectal cancer patients.
Graduate School of Business, Korea University, Seoul, Korea.
Seung-Mi
SM
Lee
Jin-Oh
JO
Kang
Yong-Moo
YM
Suh
D000465:Algorithms; D015179:Colorectal Neoplasms / Q000191:economics* / Q000453:epidemiology; D003663:Decision Trees; D017722:Hospital Charges; D006801:Humans; D015994:Incidence; D007723:Korea / Q000453:epidemiology; D017059:Models, Econometric; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests