Smart System for Early Diagnosis of Gastroesophageal Reflux Disease (GERD)

Abstract

Gastroesophageal Reflux Disease (GERD) occurs when gastric contents reflux into the esophagus, yet early diagnosis remains limited due to the lack of accessible screening tools. This limitation contributes to reduced public awareness and delays in seeking appropriate medical evaluation. To address this problem, this study aims to develop an intelligent system capable of supporting early GERD diagnosis. The proposed system evaluates stomach acid levels through saliva pH measurement and incorporates symptom assessment using the GERD-Q questionnaire, which is widely adopted by internist physicians as a clinical screening instrument. Additional variables—including lifestyle factors, age, height, and weight—are integrated into a logistic regression model to estimate the probability of GERD. The pH sensor demonstrates an accuracy of approximately 99,25%. Future studies will focus on validating the sensor data against patient medical records and comparing the system’s diagnostic performance with standard clinical examinations conducted by healthcare professionals.