Machine learning models using serum gastric biomarkers for the non-invasive prediction of atrophic gastritis: a comparative study.

Background and aims: The early, non-invasive detection of chronic atrophic gastritis (CAG), a precancerous lesion, remains a clinical challenge. While serological biomarkers are promising alternatives to endoscopy for screening, their predictive accuracy using conventional methods is suboptimal. This study aimed to identify key predictors of CAG and to comparatively develop multiple machine learning (ML) models, evaluating whether ML offers a definitive advantage and identifying a reliable model for triaging patients to endoscopy. Methods: In this retrospective diagnostic study (conducted from January to October 2020), 222 subjects (CAG prevalence: 30.6%) were stratified randomly into a training set (80%) and an independent test set (20%). Feature selection was performed exclusively on the training set using multivariate logistic regression, which identified four independent predictors: PGI, the PGI/PGII ratio, age, and anti- Results: Multivariable analysis identified the four predictors, with anti- Conclusion: Simple, interpretable linear models (Elastic Net and Logistic Regression) based on four routine clinical parameters provide a robust tool for the non-invasive identification of CAG in a clinical population referred for endoscopic evaluation. They show particular strength in ruling out disease, supporting their potential role as a triage tool. In this setting, they demonstrated more consistent performance than more complex machine learning algorithms. External validation in broader populations is warranted to confirm generalizability before clinical implementation.