Expert System for Diagnosing Lipoma Disease in Hospital Patients Latersia Using the Certainty Factor (CF) Method
Bibliographic record
Abstract
Lipoma disease is a disease characterized by a lump filled with a layer of fat that gradually accumulates under the skin, where this lump is between the skin and the muscle layer. This disease often appears on the neck, back, shoulders, arms, and thighs. In general, fat lumps or lipomas can be said to have slow growth between the skin and muscle layers. People tend to just let the lumps happen to them and think they are just normal lumps, without carrying out further examinations. The queue to see a doctor for further examination is also a factor. Therefore, it is necessary to make efforts so that the public can obtain information and be able to diagnose lipoma early without having to visit a doctor. From the description above, it is the basis for building a system that can provide information on lipoma disease and diagnose lipoma disease early. The system to be built can produce an early diagnosis analysis based on symptoms that are felt like a doctor, this system is commonly called an expert system, to support accuracy in building an expert system a method is needed in the analysis of its completion. One of the methods to be used is Certainty Factor (CF). The CF method is a clinical parameter value given by MYCIN to indicate the level of trust. The php programming language and MySQL database can build a system for diagnosing lipoma disease using the Certainty Factor method. type of lipoma Lipo Sarcoma 42.24%, Spindle cell lipoma, 56.59%, Myxoid liposarcoma 51.36%, Hibernoma 32%, Intramuscular hemangioma 51.48%, Chondroid lipoma 51.48%, Atypical lipoma 24%. From these results it can be said that the greatest confidence value is the type of Spindle cell lipoma disease with a confidence value of 56.59%.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".