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The Scientific World Journal
Volume 2013, Article ID 201976, 10 pages
Research Article

Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

1Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, 100 Shi-Chuan 1st Road, Kaohsiung 807, Taiwan
2Bureau of Health Promotion, Department of Health, No. 2 Changqing St., Xinzhuang, New Taipei City 242, Taiwan
3Department of Surgery, Kaohsiung Medical University Hospital, 100 Shi-Chuan 1st Road, Kaoshiung 807, Kaohsiung, Taiwan
4Yuan’s Hospital, No. 162 Cheng Kung 1st Road, Kaohsiung 802, Kaohsiung, Taiwan

Received 3 February 2013; Accepted 3 April 2013

Academic Editors: H.-W. Chang, Y.-H. Cheng, Y. Liu, and C.-H. Yang

Copyright © 2013 Herng-Chia Chiu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC) patients undergoing resection between artificial neural network (ANN) and logistic regression (LR) models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.