IMPLEMENTATION OF THE FUZZY TSUKAMOTO METHOD TO EARLY DETECTION TYPES OF GASTRIC DISEASES

Abstract: The stomach is a critical organ within the human digestive system, and gastric diseases represent significant health concerns often underestimated by the public. Factors such as poor dietary habits, stress, and bacterial infections contribute to these ailments, whose initial symptoms can easily be misinterpreted due to their similarity with other gastrointestinal disorders. The knowledge gap exists in the diagnostic challenges faced by both patients and healthcare professionals, necessitating the development of improved diagnostic tools. This study aims to enhance the diagnostic process for gastric diseases through the implementation of an expert system utilizing the Fuzzy Tsukamoto method. By employing questionnaires and expert interviews to gather data on symptoms, we develop a comprehensive fuzzy inference system. Results indicate that the system accurately classifies stomach ailments based on user-reported symptoms, facilitating early diagnosis and treatment. The novelty of this research lies in the application of the Fuzzy Tsukamoto method, which allows for nuanced symptom analysis and inference, providing a more accessible means for the general public to identify potential gastric diseases. Furthermore, the system design includes flowcharts, data flow diagrams, and entity relationship diagrams to ensure a user-friendly interface. The implications of this study are significant, as it offers a robust tool for early detection of gastric diseases, thereby potentially improving patient outcomes and reducing healthcare costs associated with late-stage interventions. This expert system not only assists in diagnosing gastric conditions but also serves as a valuable resource for enhancing the understanding of symptomology in gastroenterology.