Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

BACKGROUND: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients. METHODS: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers), Tehran, Iran, to predict the survival time using Cox proportional hazard and artificial neural network techniques. RESULTS: The estimated one-year, two-year, three-year, four-year and five-year survival rates of the patients were 77.9%, 53.1%, 40.8%, 32.0%, and 17.4%, respectively. The Cox regression analysis revealed that the age at diagnosis, high-risk behaviors, extent of wall penetration, distant metastasis and tumor stage were significantly associated with the survival rate of the patients. The true prediction of neural network was 83.1%, and for Cox regression model, 75.0%. CONCLUSION: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model. Therefore, this model recommended for the predicting the survival rate of these patients.