Advancing Health Diagnostics: AI-Powered CVD-REF Framework for Precise and Early Risk Assessment
Bibliographic record
Abstract
Deprivation of Critical Care systems are a major cause of fatality worldwide, highlighting it’s need for saving human lives. This study proposes a novel hybrid ensemble model, which integrates Random Forests, Gradient Boosting Machines (GBM), and Neural Networks to enhance the predictive accuracy diagnostics. The methodology combines data pre-processing, feature selection, and ensemble learning, ensuring robust and reliable predictions. Comprehensive data pre-processing includes K-Nearest Neighbours (KNN) imputation for missing values, Z-Score normalization for scaling, and Polynomial Feature Generation for non-linear feature interactions. Feature selection performed using Recursive Feature Elimination (RFE) and Mutual Information relevant variable retention. The proposed model produces 98.55% accuracy, very surpassing nine baseline models, that includes XGBoost, Random Forests, and Neural Networks. Additional metrics such as precision (97.80%), recall (98.12%), F1-Score (98.00%), and ROC-AUC (99.12%) further validate the model's robustness. This framework not only demonstrates superior accuracy but also ensures computational efficiency, making it viable for deployment in real-world healthcare settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| 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".