<![CDATA[Gastric diseases, such as gastritis, GERD, and peptic ulcers, are significant health problems with high prevalence in the community. The process of diagnosing these diseases is often hampered by symptoms that overlap with other health disorders, limited experience of medical personnel, and incomplete patient symptom data. This condition can lead to misdiagnosis and ineffective treatment. Therefore, a system is needed that can support the diagnosis process more accurately and efficiently. This study compares three intelligent system-based diagnostic methods, namely Bayes' Theorem, Case-Based Reasoning (CBR), and Dempster-Shafer Theory. Bayes' Theorem analyzes the probability of the relationship between symptoms and diseases, CBR compares new cases with previous cases, while Dempster-Shafer Theory handles data uncertainty to produce a level of diagnostic confidence. The analysis was carried out using gastric disease symptom data that has been collected from the literature and medical surveys. This study contributes by presenting a comparative analysis of the advantages and disadvantages of each method in diagnosing gastric disease. The aim is to determine the most effective method in improving diagnostic accuracy and the efficiency of the medical decision-making process. The preliminary results show a comparison between Bayes' Theorem, Case-Based Reasoning, and Dempster Shafer showing that Gastroesophageal Reflux Disease has the highest confidence level, followed by Gastritis, while Gastric Ulcer has the lowest confidence level in all methods.]]>
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