Robust Cluster-then-label (RCTL) Approach for Heart Disease Prediction
Bibliographic record
Abstract
Heart attack or stroke occurs due to the narrowed or blocked blood vessels that are present in the heart. If not identified at the early stages of the disease, there is a high probability to loose life. An algorithm that takes the advantage of the inferences given by unsupervised learning is proposed. Our proposed framework leverages the unsupervised information that is hidden in the data to improve the accuracy of the classifier. By using clustering, a label is obtained and this cluster label is included as one of the features. This augmented feature set fed as input to the classifier to predict the disease. For clustering to avoid uncertainty, k-means and spectral clustering are used. If the cluster label predicted by both the methods match, then only that information is included as one of the features. To evaluate the effectiveness of the proposed algorithm two heart disease benchmark datasets, Framingham and stat-log are used. For the classification task Logistic regression, Nave Baye's and SVM are being used to make the model simple yet effective. Based on the experimental results we claim that by augmenting the feature set with the cluster label using the proposed method significantly improves the performance of the heart disease prediction task.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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".