Ensemble learning as a prerogative method of predicting mortality of patients with cardiovascular diseases
Bibliographic record
Abstract
Among diseases that account for higher death rates, Cardio Vascular Diseases (CVD) stand forefront. Many works have been carried out since long to predict effectiveness in mortality prediction using models like data mining, logistic regression, neural networks etc. considering only traditional cardiovascular risk factors. As time and technologies evolved with incorporation of newer features these models ended up with predicted mortality rate of an accuracy 60-70%. There are many more attributes to be explored that are significant in predicting mortality rate in CVD patients, opening the scope to develop prediction models with traditional and non-traditional risk factors, much wider. This paper is focused on predicting mortality rates using three models. Each model's performance metrics are calculated to check the accuracy of the model. This helps one to build models that could best predict the outcome. Use of Ensemble learning method enhanced the prediction accuracy to 91%. This helps to validate the decision more accurately about mortality predictions and thereby assessing the risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".