Kernel Based Comparison between Fuzzy C-Means and Support Vector Machine for Sinusitis Classification
Bibliographic record
Abstract
Abstract Sinusitis is an inflammation of the sinus wall, a small cavity interconnected through the airways in the skull bones. It is located on the back of the forehead, inside the cheek bone structure, on both side of the nose, and behind the eyes. Sinusitis is caused by infection, growth of nasal polyps, allergies, and others. This condition can effect adults, teenagers, and even children. To classify sinusitis, we used Kernel Based Fuzzy C-Means, which is the development of Fuzzy C-Means (FCM). FCM algorithm groups data using Euclidean distance. However, when non-linear data is separated, the convergence is inaccurate and need a long-running time. To overcome this problem, a Kernel Based Fuzzy C-Means that use kernel functions as a substitute for Euclidean distance. It maps objects from data space to a higher dimension feature space, so they can overcome FCM deficiencies. Beside we used Kernel Based Support Vector Machine to do the same thing, that separate the data set by hyperplane. From the result of both methods, we will compare both of them to get the best method for the data set. Data that is used is sinusitis data set obtained from the laboratory of radiology at Cipto Mangunkusumo National General Hospital, Jakarta. From the experiment we got 100% accuracy of Kernel Based Fuzzy C-Means and 100% accuracy of Kernel Based Support Vector Machine using the same parameter sigma for the kernel.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.000 |
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".