title: Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models.
abstract: BACKGROUND
  In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.
  
  
  METHODS
  Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.
  
  
  RESULTS
  After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.
  
  
  CONCLUSIONS
  ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Anhui province, PR China. jwang2006@126.com
authors.forename: Jing
authors.initials: J
authors.lastname: Wang
authors.forename: Man
authors.initials: M
authors.lastname: Li
authors.forename: Yun-tao
authors.initials: YT
authors.lastname: Hu
authors.forename: Yu
authors.initials: Y
authors.lastname: Zhu
mesh_terms: D000328:Adult; D003663:Decision Trees; D005260:Female; D005544:Forecasting / Q000379:methods; D017722:Hospital Charges / Q000639:trends*; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D013274:Stomach Neoplasms / Q000191:economics*