Performance Improvement of Decision Trees for Diagnosis of Coronary Artery Disease Using Multi Filtering Approach
Why this work is in the frame
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Bibliographic record
Abstract
The heart is one of the strongest muscular organs in the human body. Every year, this disease can kill many people in the world. Coronary artery disease (CAD) is named as the most common type of heart disease. Four well-known decision trees (DTs) are applied on the Z-Alizadeh Sani CAD dataset, which consists of J48, BF tree, REP tree, and NB tree. A multi filtering approach, named MFA, was used to modify the weight of attributes to improve the performance of DTs in this study. The model was applied on three main coronary arteries including the Left Anterior Descending (LAD), Left Circumflex (LCX), and Right Coronary Artery (RCA). The obtained results show that data balancing has a valuable impact on the performance of DTs. The comparison results show that this study provides the best results applied on the Z-Alizadeh Sani dataset compared to previous studies. The proposed MFA could improve the performance of the classic DTs algorithms significantly, with the highest accuracies obtained by NB tree for LAD, LCX, and RCA are 94.92%, 90.97% and 93.43%, respectively.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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