A Next-Generation Deep Learning Model for Early Prediction of Cardiovascular Events
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) continue to be the foremost cause of mortality worldwide, emphasizing the urgent need for effective preventive diagnostic tools. Early and accurate prediction of cardiovascular events enables timely medical interventions, reduces morbidity and mortality, and assists clinicians in formulating personalized treatment plans. This research introduces Hybrid-CardioNet, a next-generation deep learning-based predictive model designed to enhance early cardiovascular event prediction through the integration of multiple learning components. The model combines Convolutional Neural Networks (CNN) for efficient spatial feature extraction, Bidirectional Long-Short Term Memory (Bi-LSTM) networks for capturing temporal dependencies across sequential clinical and physiological data, and an Attention Mechanism for prioritizing critical features influencing cardiovascular risk. For experimentation, a synthetic dataset constructed to resemble real-world patient distributions was utilized, incorporating demographic, clinical, ECG, and biochemical markers. Hybrid-CardioNet achieved superior performance with an accuracy of 96.84%, an F1-Score of 0.958, and an Area Under Curve (AUC) of 0.982, surpassing benchmark machine learning and traditional statistical models. These findings highlight the efficacy and robustness of the proposed system and demonstrate its potential utility in proactive healthcare settings, particularly for large-scale screening and automated clinical decision support to mitigate the global burden of cardiovascular diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".