Early-Stage Cardiovascular Disease Prediction Using a Sigmoidtropy-Based Decision Tree
Bibliographic record
Abstract
Heart disease (HD) is a significant health issue in the world, and its early and proper prediction is essential to minimize mortality and the development of the disease. Cardiovascular disease (CVD) is one of the diseases that need effective and stable predictive models to assist clinical decision-making. This paper gives a Sigmoidtropy-Based Decision Tree (SDT) model of cardiovascular disease prediction, which improves the traditional decision tree by adding a sigmoid-based formulation of entropy. The heart disease data are first grouped by the K-means clustering method in order to enhance the data representation. The suggested SDT model is tested on the Cleveland heart disease dataset of the UCI repository and compared to the traditional classifiers, such as Naive bayes, random forest, and the traditional Decision Tree models. Experimental findings indicate that the SDT has an accuracy of 99.67 which is better than the performance of Random Forest (76.89%), Decision Tree (76.56%), and Naive Bayes (81.84%) with a lower execution time. Despite the promising performance shown by the results, it needs further validation with more datasets and strong evaluation plans to determine the generalizability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".