ArticleInternational journal of environmental research and public health2020
Coronary Artery Disease Diagnosis; Ranking the Significant Features Using a Random Trees Model.
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it, 144 citations in OpenAlex.
- Machine Learning-Based Automated Diagnostic Systems Developed for Heart Failure Prediction Using Different Types of Data Modalities: A Systematic Review and Future Directions.Computational and mathematical methods in medicine · 2022Pooled it
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Enhancing Hypertension Risk Diagnosis Using a Hybrid Machine Learning Framework: Leveraging Body Composition Data.BioMed research international · 2026Article
- Advanced machine learning techniques for coronary artery disease risk prediction in early middle-aged adults.BMC medical genomics · 2025Article
- Feature Ranking on Small Samples: A Bayes-Based Approach.Entropy (Basel, Switzerland) · 2025Article
- Volatilome and machine learning in ischemic heart disease: Current challenges and future perspectives.World journal of cardiology · 2025Article
- Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.Scientific reports · 2025Article
- Investigating ethnic differences in risk factors and severity of developing premature coronary artery disease: Predicting the effect of risk factors through decision tree analysis in a multicenter case-control study; Results from Iran Premature Coronary Artery Disease (IPAD study).Journal of cardiovascular and thoracic research · 2025Article
- Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Coronary artery disease prediction using Bayesian-optimized support vector machine with feature selection.Frontiers in network physiology · 2025Article
- Heavy metal biomarkers and their impact on hearing loss risk: a machine learning framework analysis.Frontiers in public health · 2025Article
- Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety.Archives of public health = Archives belges de sante publique · 2024Article
- High gestational leucine level dampens WDPCP/MAPK signaling to impair the EMT and migration of cardiac microvascular endothelial cells in congenital heart defects.Pulmonary circulation · 2024Article
- The value of CCTA combined with machine learning for predicting angina pectoris in the anomalous origin of the right coronary artery.Biomedical engineering online · 2024Article
- Novel CAD Diagnosis Method Based on Search, PCA, and AdaBoostM1 Techniques.Journal of clinical medicine · 2024Article
- Tongue color parameters in predicting the degree of coronary stenosis: a retrospective cohort study of 282 patients with coronary angiography.Frontiers in cardiovascular medicine · 2024Article
- Empirical models for compressive and tensile strength of basalt fiber reinforced concrete.Scientific reports · 2023Article
- Descriptive forest: experiments on a novel tree-structure-generalization method for describing cardiovascular diseases.BMC medical informatics and decision making · 2023Article
- Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Is it the Time to Move Towards Coronary Computed Tomography Angiography-Derived Fractional Flow Reserve Guided Percutaneous Coronary Intervention? The Pros and Cons.Current cardiology reviews · 2023Review
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Heart disease is one of the most common diseases in middle-aged citizens. Among the vast number of heart diseases, coronary artery disease (CAD) is considered as a common cardiovascular disease with a high death rate. The most popular tool for diagnosing CAD is the use of medical imaging, e.g., angiography. However, angiography is known for being costly and also associated with a number of side effects. Hence, the purpose of this study is to increase the accuracy of coronary heart disease diagnosis through selecting significant predictive features in order of their ranking. In this study, we propose an integrated method using machine learning. The machine learning methods of random trees (RTs), decision tree of C5.0, support vector machine (SVM), and decision tree of Chi-squared automatic interaction detection (CHAID) are used in this study. The proposed method shows promising results and the study confirms that the RTs model outperforms other models.
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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.