ReviewCardiology research and practice2018
Automated Diagnosis of Coronary Artery Disease: A Review and Workflow.
Review in Cardiology research and practice, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Machine learning-enhanced echocardiography for screening coronary artery disease.Biomedical engineering online · 2023Trial
- Dynamic Fractional Flow Reserve from 4D-CTA: A Novel Framework for Non-Invasive Coronary Assessment.Journal of imaging · 2025Article
- Machine Learning-Based Model for Predicting Coronary Heart Disease Using Preβ HDL and Cytokines as Plasma Biomarkers.Proceedings. International Conference on Computational Science and Computational Intelligence · 2025Article
- Classification of coronary artery disease using radial artery pulse wave analysis via machine learning.BMC medical informatics and decision making · 2024Article
- Exosomal miRNA-21-5p and miRNA-21-3p as key biomarkers of myocardial infarction.Health science reports · 2024Article
- Next Generation Infectious Diseases Monitoring Gages via Incremental Federated Learning: Current Trends and Future Possibilities.Computational intelligence and neuroscience · 2023Review
- Effectiveness of SMART heart package on knowledge and practice regarding prevention of coronary artery disease among high-risk adults in selected urban community at Bangalore.Journal of education and health promotion · 2023Article
- Diagnosis of Coronary Artery Disease based on Machine Learning algorithms Support Vector Machine, Artificial Neural Network, and Random Forest.Advanced biomedical research · 2023Article
- Novel DERMA Fusion Technique for ECG Heartbeat Classification.Life (Basel, Switzerland) · 2022Article
- Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.Journal of healthcare engineering · 2022Article
- Prediction of all-cause mortality in coronary artery disease patients with atrial fibrillation based on machine learning models.BMC cardiovascular disorders · 2021Article
- Machine learning and statistical approaches for classification of risk of coronary artery disease using plasma cytokines.BioData mining · 2021Article
- RDW Value may Increase the Diagnostic Accuracy of MPS.Sisli Etfal Hastanesi tip bulteni · 2021Article
- An Analysis of the Effects of Noisy Electrocardiogram Signal on Heartbeat Detection Performance.Bioengineering (Basel, Switzerland) · 2020Article
- Minimal Patient Clinical Variables to Accurately Predict Stress Echocardiography Outcome: Validation Study Using Machine Learning Techniques.JMIR cardio · 2020Article
- Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.International journal of environmental research and public health · 2020Article
- Deep Deterministic Learning for Pattern Recognition of Different Cardiac Diseases through the Internet of Medical Things.Journal of medical systems · 2018Article
- Review
- Deep Learning Methods for Predicting Disease Status Using Genomic Data.Journal of biometrics & biostatistics · 2018Article
- Effect of an Empowerment Program on Life Orientation and Optimism in Coronary Artery Disease Patients.Iranian journal of nursing and midwifery researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.