Prediction of Hospital Charges for the Cancer Patients with Data Mining Techniques / 대한의료정보학회지

OBJECTIVE: Predictions of hospital charges for cancer patients are very important, because they provide a basis for allocating medical resources in the hospital and for establishing national medical policies. But previous studies to predict hospital charges were mainly based on statistical analysis, which has used only a small aspect among huge medical data so that the prediction power was limited. Thus we developed four data mining models, including two artificial neural network (ANN) models and two classification and regression tree (CART) models, to predict both the total amount of hospital charges and the amount paid by the insurance of cancer patients and compared their efficacies. METHODS:The data was generated from400,625 medical records of 1,605 cancer patients who had been hospitalized toKyungHeeUniversityHospital fromMarch 1, 2003 to February 29, 2004. Clementine 8.1 programwas used to build four data mining prediction models, two for the total amount and two for the amount paid by insurance. The variables included all of the data fields of standard medical record form of Korea. The neural network model used feed-forward back propagation method, which had 2 hidden layers. For decision tree model, RELIEFF method was used and the maximum tree depth was set to 30.We divided the dataset into 67%of training dataset and 33%of test dataset, using stratified sampling. Linear correlation coefficient and gain chart were compared. RESULTS: The ANN models showed better linear correlation coefficient than the CART models in predicting both the total amount (0.824 vs. 0.791) and the amount paid by insurance (0.838 vs. 0.699). The estimated accuracy of ANN model was more than 98%to predict both total amount and amount paid by insurance. The CART model for total amount showed that the relative importance of the variables were duration of admission(0.073), number of consultation(0.061), and treatment group 16(0.06). The CART model for the amount paid by insurance showed that the relative importance of the cariables were duration of admission (0.09), number of ICUadmission (0.063), and number of consultations (0.062). The percent gain of ANN model shows better %gain than CART to predict total amount but to predict amount paid by insurance, ANN showed similar pattern to CART CONCLUSION: The ANNmodels showed better prediction accuracy than CART models. However, the CART models, which serve different information from ANN model, can be used to allocate limited medical resources effectively and efficiently. For the purpose of establishing medical policies and strategies, using those models together is warranted.

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 predictthe 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.