## 6 Discussion and Conclusions
### Conclusions  
In this study, we predicted patients with atrophic gastritis who were at high risk of developing gastric cancer and analyzed some of their characteristics. For this purpose, we used DeepPrevention, which is composed of a prediction module and an explanation module, based on a deep recurrent neural network with eight layers, and applied dropout and L2 regularization. The prediction model achieves 0.84 AUC, 0.5 sensitivity, and 0.98 specificity. The explanation module identified the significant features for distinguishing between atrophic gastritis and development of gastric cancer using Chi-squared tests. Furthermore, to identify a group of patients at high risk, K-means clustering was applied to the patients predicted to develop gastric cancer. Finally, to give a personal explanation of the prediction, LIME was applied to a specified patient.  
Explainable AI has attracted much attention recently [33; 54; 55]. In particular, in medical applications, explainability is essential for both doctors and patients to understand the prediction results. In this study, we provided an explanation module, which explains the perspectives at the population, group, and individual levels. At the population level, sex, smoking status, and smoking duration were identified as influential factors. At the group level, the average diagnosis age was distinguishing factor of the high-risk group, and they were diagnosed as gastric cancer at 57 years. In addition, we identified that income, BMI, regular exercise, and the number of endoscopic screenings did not show any significant difference between groups. Finally, at the individual level, it was found that among the analyzed patient characteristics, two lifestyle habits were influential in the progression from atrophic gastritis to gastric cancer: current smoking status and smoking duration.  
Real-world medical applications often confront the problem of imbalanced data such as that encountered in this study. In the case of the extremely imbalanced data in this study, oversampling caused an overfitting problem. Furthermore, we believe that it is important to create a model that maintains the ratio of gastric cancer incidence in the real world. Therefore, we proposed a deep recurrent neural network with eight hidden layers to capture the features of the minority class; the resulting model demonstrated a sensitivity of 0.5 and a specificity of 0.98. We are currently attempting to improve its sensitivity by extending the number of patients and their features to include related chronic disease by combining other data sources. In addition, we are developing stacking ensemble learning using SVMs and random forests. As a future work, we plan to utilize _H. pylori_ infection information and consider genomic analysis.  
Conceptualization, H.H.K. and Y.S.L.; methodology, H.H.K. and Y.S.L.; software, K.J.L. and J.Y.K.; validation, S.-I.S., W.G.S. and H.H.K.; formal analysis, H.H.K.; resources, W.G.S.; data curation, S.-I.S.; writing--original draft preparation, H.H.K.; writing--review and editing, Y.S.L.; supervision, S.-I.S.; project administration, W.G.S.; funding acquisition, W.G.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Health & Welfare, Republic of Korea, and a grant from the Korea Health Technology R & D Project through the Korea Health Industry Development Institute (KHIDI) (grant number HI19C0143).