Sistem Pakar Mendiagnosa Penyakit Pada Tumor Otak Menggunakan Metode Case Based Reasoning (CBR)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This research aims to develop an expert system that can diagnose diseases related to brain tumors using the Case Based Reasoning (CBR) method. The CBR method works by comparing new cases with previous cases that have been stored in the database to provide appropriate diagnoses and treatment recommendations. This system is designed to assist medical personnel in analyzing patient symptoms, thus speeding up the process of identifying the type of brain tumor. In addition, the system is also equipped with a knowledge base obtained from real cases that have been validated by medical experts. Test results show that the diagnostic accuracy of this system reaches a fairly high level, especially in detecting frequently encountered types of brain tumors. Thus, this system has the potential to be an effective tool in the medical diagnosis process, especially in patients who show symptoms of brain tumors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it