The application of artificial neural networks and decision tree model in predicting post-operative complication for gastric cancer patients

Background/Aims: Gastric cancer remains a leading cause of death worldwide. Post-operative complication is one important factor which causes mortality of gastric cancer patients after gastrectomy. Better prediction of post-operative complication before gastrectomy can significantly reduce post-operative mortality and morbidity. Therefore, 3 data mining techniques were applied in this study on improving prediction of post-operative complication. Methodology: A retrospective study was performed on 521 patients from 3 over 2,000 acute-bed medical centers in Taiwan during February 2002 to October 2004. Pre- and post-operative clinical data were collected and analyzed by applying 3 data mining techniques, included Artificial Neural Networks (ANN), Decision Tree (DT) and Logistic Regression (LR). Results: Results of this study indicated that ANN was a better technique than DT and LR in predicting post-operative complication. Nutritious status, pathological characteristics and operational characteristics were important predictors of post-operative complication. Conclusions: Further study on predicting post-operative complication in gastric cancer patients is still important. However, how to combine different data mining techniques to improve accuracies of prediction will be another important issue for clinicians and researchers.