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Comparison of artificial neural network and logistic regression models for predicting in-hospital survival after hepatocellular carcinoma surgery

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6 Author(s)
Hon-Yi Shi ; Dept. of Healthcare Adm. & Med. Inf., Kaohsiung Med. Univ., Kaohsiung, Taiwan ; Jinn-Tsong Tsai ; Wen-Hsien Ho ; Shih-Chin Wang
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The study purposes to validate the use of ANN model for predicting in-hospital survival in (HCC) surgery patients in Taiwan and to compare the predictive accuracy of ANN with that of LR model. Patients who underwent a HCC surgery during the period from 1998 to 2009 were included in the study. This study retrospectively compared 1,000 pairs of LR and ANN models based on initial clinical data for 22,926 HCC surgery patients. For each pair of ANN and LR models, the area under the receiver operating characteristic (AUROC) curves, Hosmer-Lemeshow (H-L) statistics and accuracy rate were calculated and compared using paired T-tests. A global sensitivity analysis was also performed to assess the relative significance of input parameters in the system model and the relative importance of variables. Compared to the LR models, the ANN models had a better accuracy rate in 97.28% of cases, a better H-L statistic in 41.18% of cases, and a better AUROC curve in 84.67% of cases. Surgeon volume was the most influential (sensitive) parameter affecting in-hospital survival followed by age and lengths of stay. In comparison with the conventional LR model, the ANN mode in the study was more accurate in predicting in-hospital survival and had higher overall performance indices. 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.

Published in:

SICE Annual Conference (SICE), 2012 Proceedings of

Date of Conference:

20-23 Aug. 2012