ArticleSensors (Basel, Switzerland)2023
Long-Term Coronary Artery Disease Risk Prediction with Machine Learning Models.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Adaptation of Snyder adult dispositional hope scale into Chinese: a cross-sectional study on reliability and validity test in older adults with coronary heart disease.BMC geriatrics · 2026Article
- The Relationship and Mechanism of CD27 with Coronary Atherosclerotic Heart Disease.Cardiovascular drugs and therapy · 2026Review
- Machine Learning-Based Prediction of Coronary Artery Disease Using Clinical and Behavioral Data: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- Risk prediction for cardiovascular related diseases using PRS and EHR in the Framingham Heart Study.PloS one · 2026Article
- Anatomically Guided Cascaded U-Net Ensemble for Coronary Artery Calcification Segmentation in Cardiac CT.Bioengineering (Basel, Switzerland) · 2025Article
- Optimizing Stroke Risk Prediction: A Primary Dataset-Driven Ensemble Classifier With Explainable Artificial Intelligence.Health science reports · 2025Article
- Optimized machine learning mechanism for big data healthcare system to predict disease risk factor.Scientific reports · 2025Article
- A Real-Time Computer-Aided Diagnosis System for Coronary Heart Disease Prediction Using Clinical Information.Reviews in cardiovascular medicine · 2025Article
- Article
- Using Life's Essential 8 and heavy metal exposure to determine infertility risk in American women: a machine learning prediction model based on the SHAP method.Frontiers in endocrinology · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Construction of machine learning diagnostic models for cardiovascular pan-disease based on blood routine and biochemical detection data.Cardiovascular diabetology · 2024Article
- Visualization obesity risk prediction system based on machine learning.Scientific reports · 2024Article
- Efficient Extraction of Coronary Artery Vessels from Computed Tomography Angiography Images Using ResUnet and Vesselness.Bioengineering (Basel, Switzerland) · 2024Article
- Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework.Frontiers in cardiovascular medicine · 2024Article
- Analyzing the impact of feature selection methods on machine learning algorithms for heart disease prediction.Scientific reports · 2023Article
- Developing a hypertension visualization risk prediction system utilizing machine learning and health check-up data.Scientific reports · 2023Article
- An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study.Cardiovascular diabetology · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heart is the most vital organ of the human body; thus, its improper functioning has a significant impact on human life. Coronary artery disease (CAD) is a disease of the coronary arteries through which the heart is nourished and oxygenated. It is due to the formation of atherosclerotic plaques on the wall of the epicardial coronary arteries, resulting in the narrowing of their lumen and the obstruction of blood flow through them. Coronary artery disease can be delayed or even prevented with lifestyle changes and medical intervention. Long-term risk prediction of coronary artery disease will be the area of interest in this work. In this specific research paper, we experimented with various machine learning (ML) models after the use or non-use of the synthetic minority oversampling technique (SMOTE), evaluating and comparing them in terms of accuracy, precision, recall and an area under the curve (AUC). The results showed that the stacking ensemble model after the SMOTE with 10-fold cross-validation prevailed over the other models, achieving an accuracy of 90.9 %, a precision of 96.7%, a recall of 87.6% and an AUC equal to 96.1%.
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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.