Sistem Pakar Diagnosa Penyakit Hipertrofi hidung Menggunakan Metode Certainty Factor
Bibliographic record
Abstract
Nasal hypertrophy is a swelling that occurs in the nasal concha. This condition is caused because the inferior concha has a larger anatomical size when compared to the other concha structures. The process of diagnosing nasal hypertrophy often requires high clinical skills and experience. RSU Putri Bidadari is one of the hospitals that treats Nasal Hypertrophy disease in patients. Nose hypertrophy disease has several symptoms that are felt which are usually caused by several factors such as exposure to certain allergens, chronic sinus infections, or a family history of similar nasal problems, so several diagnostic tests are needed that can confirm the diagnosis, such as nasal endoscopy to see directly the condition inside the nose, medical imaging such as CT scan or MRI to evaluate the structure of the nose in more detail, or allergy tests to identify the causative allergen. From the above problems, patients really need a system that becomes a recommendation in helping provide information about nasal hypertrophy disease that can diagnose early and take further action to prevent nasal hypertrophy disease. By using the certainty factor method, information from the steps above can be systematically analyzed to determine the level of confidence in the diagnosis of nasal hypertrophy. These factors can be assessed based on severity, presence of typical symptoms, correlation with risk factors, and results of physical examination and diagnostic tests. Based on the results of the CF calculation, the highest value is in the type of nasal hypertrophy disease with the type of Septal Deviation disease having a value of 1 or 100%, in the type of Rhinitis disease having a value of 94.24% and in the type of sinusitis disease having a value of 85.60%. From the results obtained, the system identifies that the patient has nasal hypertrophy with Septal Deviation type by 100%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".