An interpretable classifier for detection of cardiac arrhythmias by using the fuzzy decision tree
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
An extraction of medical knowledge from cardiological data is proposed in this work, it is based on relevant intelligent method called fuzzy decision tree. It could lead to increase understanding the cause of various abnormal beats in cardiac activity, leading to a better medical diagnosis. The performance of this technique is evaluated on the MIT-BIH Arrhythmia Database following the AAMI recommendations. The first part of this paper discusses the characterization of heart beats. It is considered as an important step in arrhythmias classification. In a second part we apply the fuzzy decision tree to recognize some cardiac abnormalities. In the last part we discuss the activity of fuzzy decision rules extracted from cardiological data analyzing.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 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