Stratification of Cardiovascular Diseases Using Deep Learning
Bibliographic record
Abstract
Heart-based diseases are one of the causes for major death rate in the world. WHO (World Health Organization) specified that 17 million of people are losing their lives per year due to several heart diseases. Artificial Intelligence playing a prominent role in disease identification and prediction from medical data. Magnetic Resonance Imaging plays a vital role in producing detailed images of internal organs and soft tissues for better understanding the condition. Magnetic Resonance Image contains more noisy data this is one of the issues to be addressed, hence this research focuses on the prediction of cardiovascular diseases using an innovative hybrid algorithm and addresses the issue of noise using Hann filters. A Hybrid algorithm is proposed with combination of Cat Fuzzy Neural Model (CFuNM) and Hybrid Ant Colony and African Buffalo Optimization. Cat Fuzzy Neural Model (CFuNM) is used to classify cardiac diseases such as cardiomyopathy, pericardial effusion, coronary artery, amyloidosis, and other coronary heart diseases and for the severity analysis of disease we used Hybrid Ant Colony and African Buffalo Optimization (HAC-ABO) mechanism. This research of Hybrid deep learning model improved the classification accuracy of 99.3% and error rate of 0.18% which is considerably good when compared to existing methods.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".