Machine Learning Application to Predict the Risk of Coronary Artery Atherosclerosis
Bibliographic record
Abstract
Coronary artery disease is the leading cause of death in the world. In this research, we propose an algorithm based on the machine learning techniques to predict the risk of coronary artery atherosclerosis. A ridge expectation maximization imputation (REMI) technique is proposed to estimate the missing values in the atherosclerosis databases. A conditional likelihood maximization method is used to remove irrelevant attributes and reduce the size of feature space and thus improve the speed of the learning. The STULONG and UCI databases are used to evaluate the proposed algorithm. The performance of heart disease prediction for two classification models is analyzed and compared to previous work. Experimental results show the improved accuracy percentage of risk prediction of our proposed method compared to other works. The effect of missing value imputation on the prediction performance is also evaluated and the proposed REMI approach performs significantly better than conventional techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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; both teacher heads agree on what is shown here.
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".