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Published in: Journal of Gastrointestinal Surgery 11/2012

01-11-2012 | Original Article

Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study

Authors: Hon-Yi Shi, King-Teh Lee, Jhi-Joung Wang, Ding-Ping Sun, Hao-Hsien Lee, Chong-Chi Chiu

Published in: Journal of Gastrointestinal Surgery | Issue 11/2012

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Abstract

Background

To 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.

Methods

This 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.

Results

Compared 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.

Conclusions

In 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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Metadata
Title
Artificial Neural Network Model for Predicting 5-Year Mortality After Surgery for Hepatocellular Carcinoma: A Nationwide Study
Authors
Hon-Yi Shi
King-Teh Lee
Jhi-Joung Wang
Ding-Ping Sun
Hao-Hsien Lee
Chong-Chi Chiu
Publication date
01-11-2012
Publisher
Springer-Verlag
Published in
Journal of Gastrointestinal Surgery / Issue 11/2012
Print ISSN: 1091-255X
Electronic ISSN: 1873-4626
DOI
https://doi.org/10.1007/s11605-012-1986-3

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