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

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.