In our study, the ANN model and decision tree model of two datasets were compared in terms of mean absolute errors and linear correlation coefficients. It was found that: for both the training and the test datasets, mean absolute errors of ANN model were lower than those of decision tree model and linear correlation coefficients of the former were higher than those of the latter. The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.
Seung-Mi Lee  conducted a similar study, in which the prediction efficients of ANN and decision tree on the hospital charge on colon cancer patients in Korean were compared. Lee compared ANN model and decision tree model using training and test datasets generated from two groups of patients with different payment schemes, i.e. payment by patients themselves and by public insurance. Lee's study revealed that the prediction efficient of ANN model was superior to the decision tree model for patients whose hospital charge was paid by public insurance; but it was difficult to measure which model was better than the other for patients who paid the hospital charge by themselves. We think that the reason may be as follows: if hospital charges is to be covered by public insurance, the treatment could be performed based on needs of progress of the disease and thus lead to more "reasonable" hospital charges; on the contrary, if hospital charge is to be paid by patients themselves, the treatment could be interfered by some subject factors of the patients, and thus lead to uneven compositions of hospital charges. Given these Lee still concluded that the prediction efficient of ANN model for the hospital charge on colon cancer patients was superior to the decision tree model.
In our study, 18.85% of the patients paid the charges by themselves and 81.15%, by public insurance. Payment system was not found to have significant effects on the hospital charges on gastric cancer patients in the fitness of the two models. This is inconsistent with the report by Lim JH . So we did not compare the two models for these two different groups of patients.
It was found, from the results of the fitness of ANN model, that length of stay was the most important factor on the hospital charge on gastric cancer patients. One explanation for this may be that inpatients' medication was not interrupted and bed charge was inevitable; at the same time, the major components of the hospital charge were just medicine and treatment charges. Furthermore, operation, emergency treatments, type of operation and times of being rescued were also important factors on the hospital charge of gastric cancer patients.
In spite that the prediction efficient of decision tree model was found inferior to ANN model in our study, the method provides directly visible classification rules. Acting on these classification rules, the hospital charge on gastric cancer patients could be easily controlled so as to avoid the phenomenon of inappropriate services.
Additionally we should consider that, as a "black box", BP in ANN would hide some effects of any possible interactions, which was the limitation of this method.
Of course, the gastric cancer patients were drawn only from one hospital in Anhui province of China. If more information of the hospital charge of gastric cancer patients was collected from every region of China, the results should be more reliable. Moreover, given the arithmetic traits of ANN and decision tree models, the choice of predictive models could be performed depending on different research emphasis; or the two models could be used in combination.