Determining of Prognostic Factors in Gastric Cancer PatientsUsing Artificial Neural Networks

Background &
Objectives: The aim of this study is to determine diagnostic factors for Iranian gastric cancerpatients and their importance using artificial neural network and Weibull regression models.
Methods: Thisstudy was a historical cohort study with data gathered from 436 registered gastric cancer patients who underwentsurgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal cancers), Tehran,Iran. In order to determine risk factors and their importance, neural network and Weibull regression modelswere used.
Results: The Weibull regression analysis showed that lymph node metastasis and histopathology oftumor were selected as important variables. Based on the neural network model, staging, lymph node metastasis,histopathology of tumor, metastasis, and age at diagnosis were selected as important variables. The true predictionof neural network was 82.6%, and for the Weibull regression model, 75.7%.
Conclusion: The present studyshowed that the neural network model is a more powerful tool in determining the important variables for gastriccancer patients compared to Weibull regression model. Therefore, this model is recommended for determiningof risk factors of such patients.