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Open Access Research article

Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

Jing Wang1*, Man Li2, Yun-tao Hu3 and Yu Zhu4

Author Affiliations

1 Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Anhui province, PR China

2 Department of Medical Information, the First Affiliated Hospital, Anhui Medical University, Anhui province, PR China

3 Department of Clinical Medicine, Anhui Medical University, Anhui province, PR China

4 Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, Anhui province, PR China

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BMC Health Services Research 2009, 9:161  doi:10.1186/1472-6963-9-161

Published: 14 September 2009

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.

Conclusion

ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.