• Hon-Yi Shi, King-Teh Lee, Jhi-Joung Wang, Ding-Ping Sun, Hao-Hsien Lee, and Chong-Chi Chiu.
• Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, Kaohsiung, Taiwan.
• J. Gastrointest. Surg. 2012 Nov 1;16(11):2126-31.

BackgroundTo validate the use of artificial neural network (ANN) models for predicting 5-year mortality in HCC and to compare their predictive capability with that of logistic regression (LR) models.MethodsThis study retrospectively compared LR and ANN models based on initial clinical data for 22,926 HCC surgery patients from 1998 to 2009. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and to rank the importance of variables.ResultsCompared to the LR models, the ANN models had a better accuracy rate in 96.57 % of cases, a better Hosmer-Lemeshow statistic in 0.34 of cases, and a better receiver operating characteristic curves in 88.51 % of cases. Surgeon volume was the most influential (sensitive) parameter affecting 5-year mortality followed by hospital volume and Charlson co-morbidity index.ConclusionsIn comparison with the conventional LR model, the ANN model in this study was more accurate in predicting 5-year mortality. Further studies of this model may consider the effect of a more detailed database that includes complications and clinical examination findings as well as more detailed outcome data.

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