Framework Two-Tier Feature Selection on the Intelligence System Model for Detecting Coronary Heart Disease
Bibliographic record
Abstract
Coronary heart disease is a non-communicable disease with high mortality. A good action to anticipate this is to do prevention, namely by carrying out a healthy lifestyle and routine early examinations. Early detection of coronary heart disease requires a number of examinations, such as demographics, ECG, laboratory, symptoms, and even angiography. The number of inspection parameters in the context of early detection will have an impact on the time and costs that must be incurred. Selection of the right and important inspection parameters will save time and costs. This study proposes an intelligence system model for the detection of coronary heart disease by using a minimal examination attribute, with performance in the good category. This research method is divided into a number of stages, namely data normalization, feature selection, classification, and performance analysis. Feature selection uses a Two-tier feature selection framework consisting of correlation-based filters and wrappers. The system model is tested using a number of datasets, and classification algorithms. The test results show that the proposed two-tier feature selection framework is able to reduce the highest attribute of 73.51% in the z-Alizadeh Sani dataset. The performance of the system using the bagging-PART algorithm is able to provide the best performance with parameters area under the curve (AUC) 95.4%, sensitivity 95.9% while accuracy is 94.1%. Referring to the AUC value, the proposed system model is included in the good category.
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How this classification was reachedexpand
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".