Expert System for Diagnosing Gastric Diseases with the Application of the Fuzzy Logic Sugeno Method (Case Study: Delia General Hospital)
Bibliographic record
Abstract
Stomach diseases, such as gastritis, GERD, and gastric ulcers, are digestive disorders with a high prevalence and have the potential to reduce the quality of life of sufferers. Conventional diagnosis still faces obstacles, including limited medical personnel, the length of examination procedures, and the similarity of symptoms between diseases. This study developed a web-based expert system with the Fuzzy Logic Sugeno method to assist in the early diagnosis of gastric diseases. Symptom data was obtained from literature and consultation with specialist doctors at Delia General Hospital. The system is designed with the stages of needs analysis, UML modeling, database design, and the implementation of fuzzy algorithms (fuzzification, inference, defuzzification). The simulation results showed that the system was able to provide diagnostic recommendations with a membership rate of 28% for gastritis, 62% for GERD, and 80% for gastric ulcers. The implementation of a web-based interface allows users to select the symptoms they are experiencing, then the system displays the results of the diagnosis along with their severity. This study shows that the Fuzzy Sugeno method is effective in handling vague symptom data, as well as acting as a consistent and efficient early diagnosis tool.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".