ObjectivesGastroesophageal reflux-related chronic cough (GERC), an extraesophageal manifestation of gastroesophageal reflux disease (GERD). Although 24h MII-pH monitoring is the gold standard for diagnosing GERC, its invasiveness, high cost, and limited accessibility hinder widespread use in many clinical settings. This study aimed to develop a non-invasive machine learning model incorporating Peptest™ and GerdQ scores to facilitate GERC detection, particularly in primary care and resource-limited environments where MII-pH testing is not readily available.Methods210 chronic cough patients were enrolled between September 2022 and June 2024. GERC diagnosis followed established guidelines, and salivary pepsin levels were measured via Peptest™. Feature selection was performed using the Boruta algorithm (hereafter referred to as Boruta), a method based on random forest (RF), designed to identify relevant variables by comparing them to random shadow features. The selected optimal features were then evaluated using nine ML models, including logistic regression (LR), RF and others. Model performance was assessed through area under the curve (AUC), decision curve analysis (DCA), and calibration curves.Results73 (34.76%) patients had GERC. Peptest™ and GerdQ scores were key predictors. Logistic regression was selected for its balance of accuracy (AUC: 0.876) and clinical utility. The nomogram model showed excellent discrimination and calibration. DCA indicated high net benefit at prediction thresholds of 0.10-0.90. RCS analysis revealed non-linear relationships: GERC risk increased with GerdQ >8.66 and Peptest™ >54.791 ng/ml.ConclusionThe nomogram model provides a reliable, non-invasive tool for GERC diagnosis, aiding timely clinical intervention, especially for patients unsuitable for pH testing.