## 5 Conclusions  
This study significantly contributes to the field by developing a robust predictive model for chronic atrophic gastritis (CAG) using non-invasive indicators. The model, based on a comprehensive analysis of electronic medical records from 20,615 patients, incorporates factors such as age, hematocrit, and platelet distribution width, identified as significant predictors of CAG. By leveraging the Random Forest algorithm, which outperformed other machine learning approaches in terms of accuracy, precision, recall, and F1-score, this study not only enhances the predictive capability but also provides a practical tool that could be integrated into clinical decision support systems. This predictive model represents a significant advancement in aiding the early diagnosis and screening of chronic atrophic gastritis, with potential implications for improving patient management and treatment strategies. Although the predictive model developed in this study represents a significant advancement, there are still a number of areas that require further attention for effective implementation. Currently, the integration of the model into clinical workflow and decision support systems remains a serious challenge. Several factors need to be addressed to implement this strategy, including training healthcare professionals to effectively interpret and utilize the model's outputs, ensuring system compatibility with existing EHR systems, and validating the model in different clinical settings to confirm its generalizability. In addition, the ongoing evaluation of patient outcomes and feedback from healthcare providers is critical to refining and improving the model. Future research should focus on these aspects to improve the practical application and impact of the predictive model in real clinical settings. From a future research perspective, we suggest that the accuracy and robustness of predictions can be improved by exploring more modern machine learning algorithms; techniques such as deep learning or ensemble methods can be used to gain more insight into the factors influencing chronic atrophic gastritis. In addition, the integration of methods to improve the interpretability of results, such as interpretable artificial intelligence techniques, is essential to increase the transparency and clinical utility of models. Future studies should also focus on expanding datasets to include diverse populations and incorporating longitudinal data to assess model performance over time. These advances will help refine predictive models and their practical application in clinical settings. Conceptualization, X.Z. ; methodology, X.Z. ; resources, G.W. and J.W. ; data curation, X.Z. ; writing--original draft preparation, X.Z. ; writing--review and editing, M.Z., G.W. and J.W. ; visualization, M.Z. ; supervision, G.W. and J.W. ; project administration, M.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN2020100434), the research foundation of Chongqing University of Science and Technology (Grant No. 182201038), and the Intelligent Medicine Program of Chongqing Medical University (Grant No. ZHYX202120). **Institutional Review Board Statement:** This study was conducted in accordance with the Declaration of Helsinki and was approved by Chongqing Medical University (2023083 7 November 2023). **Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. **Data Availability Statement:** The data that support the findings of this study are not publicly available due to privacy and ethical restrictions. Access to the data is restricted to ensure the confidentiality and privacy of the participants involved in the study. Any inquiries regarding the data can be directed to the corresponding author.