A Novel Ensemble Deep Learning Model for Coronary Heart Disease Prediction
Bibliographic record
Abstract
In the last decade, heart diseases have become the leading cause of deaths in the world.Various risk factors associated with heart disease include age, gender, cholesterol levels, blood pressure, glucose levels, chest pain, obesity, stress, family history, etc. with the help of which heart diseases can be predicted in any patient.In the past decade or so various efforts have been made by the researchers for effective prediction of various heart diseases.In this paper, a novel ensemble deep learning model has been proposed for efficient prediction of coronary heart disease.The dataset used for this purpose is collected from the Framingham heart disease database.Different performance evaluation metrics including precision, accuracy, recall and f1-score are being used for evaluating the performance of the proposed model.As per the experimental results, the proposed ensemble model outperformed most of the conventional machine learning techniques in terms of accuracy, precision, recall and f1 score for coronary heart disease prediction.
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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 | 0.002 | 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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".