Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients

Zhu,Lucheng; Luo,Wenhua; Su,Meng; Wei,Hangping; Wei,Juan; Zhang,Xuebang; Zou,Changlin

doi:10.3892/br.2013.140

Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients

Authors:
- Lucheng Zhu
- Wenhua Luo
- Meng Su
- Hangping Wei
- Juan Wei
- Xuebang Zhang
- Changlin Zou
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Affiliations: Department of Radio‑Chemotherapy Oncology, The First Affiliated Hospital of Wenzhou Medical College, Wenzhou, Zhejiang 325002, P.R. China
Published online on: July 18, 2013 https://doi.org/10.3892/br.2013.140
Pages: 757-760

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Abstract

The aim of this study was to determine the prognostic factors and their significance in gastric cancer (GC) patients, using the artificial neural network (ANN) and Cox regression hazard (CPH) models. A retrospective analysis was undertaken, including 289 patients with GC who had undergone gastrectomy between 2006 and 2007. According to the CPH analysis, disease stage, peritoneal dissemination, radical surgery and body mass index (BMI) were selected as the significant variables. According to the ANN model, disease stage, radical surgery, serum CA19‑9 levels, peritoneal dissemination and BMI were selected as the significant variables. The true prediction of the ANN was 85.3% and of the CPH model 81.9%. In conclusion, the present study demonstrated that the ANN model is a more powerful tool in determining the significant prognostic variables for GC patients, compared to the CPH model. Therefore, this model is recommended for determining the risk factors of such patients.

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