Sistem Pakar Mendiagnosa Penyakit Pada Gangguan Pernafasan Menggunakan Metode Naïve Bayes
Bibliographic record
Abstract
Respiratory tract disease is a common condition that can affect anyone regardless of age. Starting from relatively mild symptoms to alarming symptoms. Although some respiratory diseases are not life-threatening, they should not be taken lightly as they can cause serious complications. What often happens is that it is difficult for a patient to see a specialist doctor because of the limited number of respiratory specialists who cannot fully serve patients, so people often have difficulty if they want to consult directly. This triggers the habit of the community to treat complaints on their own with simple drugs bought freely at drugstores or pharmacies without knowing for sure the disease they suffer, as well as the length of waiting for queues, consultation fees that are quite expensive and not everyone has a short distance to the hospital prefer not to go to a specialist. Like other organs of the human body, breathing is also prone to various diseases. Respiratory organs will be disrupted and can even cause death. By using the Naïve Bayes method above, it is known that the diagnosis of respiratory disease is that the young female patient is diagnosed with a type of respiratory disease called Farangitis (P05) with a percentage of 47.44%.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".