ArticleJournal of healthcare engineering2022
Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Machine Learning-Based Prediction of Coronary Artery Disease Using Clinical and Behavioral Data: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Development and Validation of a Multi-Omics Model Integrating US-Derived and WSI-Based Features to Predict Lymph Node Metastasis in Ovarian Cancer: A Multi-Center Retrospective Study.International journal of women's health · 2026Article
- PSO-BiLSTM-Attention: An Interpretable Deep Learning Model Optimized by Particle Swarm Optimization for Accurate Ischemic Heart Disease Incidence Forecasting.Bioengineering (Basel, Switzerland) · 2025Article
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Prediction of clinical stages of cervical cancer via machine learning integrated with clinical features and ultrasound-based radiomics.Scientific reports · 2025Article
- Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease.Scientific reports · 2025Article
- Risk-based evaluation of machine learning-based classification methods used for medical devices.BMC medical informatics and decision making · 2025Article
- Coronary artery disease classification using ConvMixer based classifier from CT angiography images.PeerJ. Computer science · 2025Article
- Exosomal miRNA-21-5p and miRNA-21-3p as key biomarkers of myocardial infarction.Health science reports · 2024Article
- An Integrated Machine Learning Approach for Congestive Heart Failure Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- A comparative study of CNN-capsule-net, CNN-transformer encoder, and Traditional machine learning algorithms to classify epileptic seizure.BMC medical informatics and decision making · 2024Article
- Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data.BMC medical informatics and decision making · 2024Article
- A comprehensive radiopathological nomogram for the prediction of pathological staging in gastric cancer using CT-derived and WSI-based features.Translational oncology · 2024Article
- Machine Learning Technology for EEG-Forecast of the Blood-Brain Barrier Leakage and the Activation of the Brain's Drainage System during Isoflurane Anesthesia.Biomolecules · 2023Article
- Data Mining and Fusion Framework for In-Home Monitoring Applications.Sensors (Basel, Switzerland) · 2023Article
- Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients.Healthcare (Basel, Switzerland) · 2023Article
- Disulfidptosis-associated lncRNAs predict breast cancer subtypes.Scientific reports · 2023Article
- An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study.Cardiovascular diabetology · 2023Article
- Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEVFrontiers in physiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In today's industrialized world, coronary artery disease (CAD) is one of the leading causes of death, and early detection and timely intervention can prevent many of its complications and eliminate or reduce the resulting mortality. Machine learning (ML) methods as one of the cutting-edge technologies can be used as a suitable solution in diagnosing this disease. Methods: In this study, different ML algorithms' performances were compared for their effectiveness in developing a model for early CAD diagnosis based on clinical examination features. This applied descriptive study was conducted on 303 records and overall 26 features, of which 26 were selected as the target features with the advice of several clinical experts. In order to provide a diagnostic model for CAD, we ran most of the most critical classification algorithms, including Multilayer Perceptron (MLP), Support Vector Machine (SVM), Logistic Regression (LR), J48, Random Forest (RF), K-Nearest Neighborhood (KNN), and Naive Bayes (NB). Seven different classification algorithms with 26 predictive features were tested to cover all feature space and reduce model error, and the most efficient algorithms were identified by comparison of the results. Results: Based on the compared performance metrics, SVM (AUC = 0.88, F-measure = 0.88, ROC = 0.85), and RF (AUC = 0.87, F-measure = 0.87, ROC = 0.91) were the most effective ML algorithms. Among the algorithms, the KNN algorithm had the lowest efficiency (AUC = 0.81,
Indexed as
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.