Towards Diagnostic Aided Systems in Coronary Artery Disease Detection: A Comprehensive Multiview Survey of the State of the Art
Bibliographic record
Abstract
Introduction . Coronary artery disease (CAD) is one of the main causes of death all over the world. One way to reduce the mortality rate from CAD is to predict its risk and take effective interventions. The use of machine learning‐ (ML‐) based methods is an effective method for predicting CAD‐induced death, which is why many studies in this field have been conducted in recent years. Thus, this study aimed to review published studies on artificial intelligence classification algorithms in CAD detection and diagnosis. Methods . This study systematically reviewed the most cutting‐edge techniques for analyzing clinical and paraclinical data to quickly diagnose CAD. We searched PubMed, Scopus, and Web of Science databases using a combination of related keywords. A data extraction form was used to collect data after selecting the articles based on inclusion and exclusion criteria. The content analysis method was used to analyze the data, and based on the study’s objectives, the results are presented in tables and figures. Results . Our search in three prevalent databases resulted in 15689 studies, of which 54 were included to be reviewed for data analysis. Most studies used laboratory and demographic data classification and have shown desirable results. In general, three ML methods (traditional ML, DL/NN, and ensemble) were used. Among the algorithms used, random forest (RF), linear regression (LR), neural networks (NNs), support vector machine (SVM), and K‐nearest networks (KNNs) have the most applications in the field of code recognition. Conclusion . The findings of this study show that these models based on different ML methods were successful despite the lack of a benchmark for comparing and analyzing ML features, methods, and algorithms in CAD diagnosis. Many of these models performed better in their analyses of CAD features as a result of a closer look. In the near future, clinical specialists can use ML‐based models as a powerful tool for diagnosing CAD more quickly and precisely by looking at its design’s technical facets. Among its incredible outcomes are decreased diagnostic errors, diagnostic time, and needless invasive diagnostic tests, all of which typically result in decreases in diagnostic expenses for healthcare systems.
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 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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.020 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".