Currently, the healthcare system is facing many challenges, however computer-aided disease diagnosis might play an essential role in enhancing the quality of medical services and relief these challenges. The aim of this work consists of two parts, where in the first part, we identified the feasible machine learning methods for stomach disease diagnosis, that can be incorporated into Remote Diagnosis and Support Medical System developed by our research team. For the study, a medical dataset with over 1000 instances and 24 attributes for five stomach disorders was used. During the testing process, the implemented machine learning algorithms achieved an accuracy of 98% (p<0.001) for the logistic regression model, and R2 of 0.88 (p<0.001) for multivariate linear regression model respectively. We have concluded that both machine learning methods are sufficient for stomach disease diagnosis and were integrated into the mentioned system. In the second part, we performed a statistical data analysis for prevalence and critical factor analysis. The risk factors such as painkillers consumption, stress and dental problems found to have a high correlation with stomach disorders and symptoms like nausea and abdominal pain might be an important precursor of a particular stomach disease.