ArticleFrontiers in cardiovascular medicine2021
Development and Validation of a Predictive Model for Coronary Artery Disease Using Machine Learning.
Article in Frontiers in cardiovascular medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 citations in OpenAlex.
- Comparing machine learning models and traditional approaches for predicting obstructive coronary artery disease: a systematic review and meta-analysis.Frontiers in cardiovascular medicine · 2026Pooled it
- Characterization and risk stratification of coronary artery disease in people living with HIV: a global systematic review.Frontiers in cardiovascular medicine · 2025Pooled it
- Machine learning-based model for identifying liver injury in patients with thyroid-associated ophthalmopathy.International ophthalmology · 2026Article
- Multi-Omics and Artificial Intelligence in Cardiovascular Medicine: From Mechanistic Insights to Clinical Translation.Biomedicines · 2026Review
- Explainable machine learning for predicting coronary heart disease risk in patients with carotid atherosclerosis: A retrospective study with SHAP and decision curve analysis.Journal of clinical and translational science · 2026Article
- Machine learning in developing a predictive model for chronic hydrocephalus following aneurysmal subarachnoid hemorrhage.Frontiers in neurology · 2025Article
- Construction of a risk prediction model for pulmonary infection in patients with spontaneous intracerebral hemorrhage during the recovery phase based on machine learning.Frontiers in neurology · 2025Article
- Enhanced machine learning and hybrid ensemble approaches for Coronary Heart Disease prediction.PloS one · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Predicting early-stage coronary artery disease using machine learning and routine clinical biomarkers improved by augmented virtual data.European heart journal. Digital health · 2024Article
- Development of a clinical-radiological nomogram for predicting severe postoperative peritumoral brain edema following intracranial meningioma resection.Frontiers in neurology · 2024Article
- Construction and Validation of a Predictive Model for Coronary Artery Disease Using Extreme Gradient Boosting.Journal of inflammation research · 2024Article
- Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.Frontiers in cardiovascular medicine · 2024Article
- Cardiovascular disease incidence prediction by machine learning and statistical techniques: a 16-year cohort study from eastern Mediterranean region.BMC medical informatics and decision making · 2023Article
- Machine learning prognosis model based on patient-reported outcomes for chronic heart failure patients after discharge.Health and quality of life outcomes · 2023Article
- Article
- Identification of DNA methylation-regulated genes as potential biomarkers for coronary heart disease via machine learning in the Framingham Heart Study.Clinical epigenetics · 2022Article
- Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn's disease.Frontiers in genetics · 2022Article
- Prediction of Atrial Fibrillation in Hospitalized Elderly Patients With Coronary Heart Disease and Type 2 Diabetes Mellitus Using Machine Learning: A Multicenter Retrospective Study.Frontiers in public health · 2022Article
- Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification of coronary artery disease (CAD) can prevent the progress of CAD and effectually lower the mortality rate, so we intended to construct and validate a machine learning model to predict the risk of CAD based on conventional risk factors and lab test data. There were 3,112 CAD patients and 3,182 controls enrolled from three centers in China. We compared the baseline and clinical characteristics between two groups. Then, Random Forest algorithm was used to construct a model to predict CAD and the model was assessed by receiver operating characteristic (ROC) curve. In the development cohort, the Random Forest model showed a good AUC 0.948 (95%CI: 0.941-0.954) to identify CAD patients from controls, with a sensitivity of 90%, a specificity of 85.4%, a positive predictive value of 0.863 and a negative predictive value of 0.894. Validation of the model also yielded a favorable discriminatory ability with the AUC, sensitivity, specificity, positive predictive value, and negative predictive value of 0.944 (95%CI: 0.934-0.955), 89.5%, 85.8%, 0.868, and 0.886 in the validation cohort 1, respectively, and 0.940 (95%CI: 0.922-0.960), 79.5%, 94.3%, 0.932, and 0.823 in the validation cohort 2, respectively. An easy-to-use tool that combined 15 indexes to assess the CAD risk was constructed and validated using Random Forest algorithm, which showed favorable predictive capability (http://45.32.120.149:3000/randomforest). Our model is extremely valuable for clinical practice, which will be helpful for the management and primary prevention of CAD patients.
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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.