ArticleScientific reports2025
Predicting coronary heart disease with advanced machine learning classifiers for improved cardiovascular risk assessment.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Development of AI based behavioral feature patterns on influencing asymptomatic cardiovascular disease attributes: a dataset standardization approach.Scientific reports · 2026Article
- Prognostic Modeling of Tricuspid Valve Regurgitation Outcomes Using Machine Learning-Based Survival Analysis.Journal of clinical medicine · 2026Article
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Geospatial and machine learning analyses of cardiovascular disease mortality across the continental United States: Identifying associated variables using Shapley values.BMC public health · 2026Article
- VMamba-QAG-net: a five-stage pipeline for SYNTAX score computation and decision support in interventional cardiology using X-ray angiography.Frontiers in artificial intelligence · 2026Article
- Deep transfer learning for breast cancer detection in underserved regions.Frontiers in oncology · 2026Article
- A comparative performance analysis of ensemble learning and regularized neural networks in cardiovascular risk prediction.Frontiers in medical technology · 2026Article
- Quantum-optimized spatio-temporal transformer for multimodal cardiovascular risk prediction from clinical and wearable data.Frontiers in cardiovascular medicine · 2026Article
- CardiaTics: An explainable AI integrated heart disease diagnosis model with feature engineering and stacked ensemble approach.Journal of big data · 2026Article
- Evaluation of coronary heart disease risk prediction based on simple physical examination parameters by machine learning model: a retrospective cohort model development and validation study.Frontiers in cardiovascular medicine · 2026Article
- Using convolutional neural networks with late fusion to predict heart disease.Scientific reports · 2025Article
- The Evolution of Machine Learning and Its Applications in Orthopaedics: A Bibliometric Analysis.Cureus · 2025Review
- Predicting SARS-CoV-2-specific CD4Clinical and experimental medicine · 2025Article
- Coronary heart disease risk prediction based on GAIN imputation and interpretable machine learning.Frontiers in genetics · 2025Article
- A systematic review of machine learning in heart disease prediction.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Potential legacy effect of digital therapeutics on mortality in CHD patients treated with PCI: 8-Year result of BAMA trial.Digital healthArticle
Corrections and comments
- Erratum issued
Authors and funding
8 authors.
Funding
Abstract
Worldwide, coronary heart disease (CHD) is a leading cause of mortality, and its early prediction remains a critical challenge in clinical data analysis. Machine learning (ML) offers valuable diagnostic support by leveraging healthcare data to enhance decision-making and prediction accuracy. Although numerous studies have applied ML classifiers for heart disease prediction, their contributions often lack clarity in addressing key challenges. In this paper, we present a comprehensive ML framework that systematically tackles these issues. First, we employ mutual information (MI) for effective feature selection to isolate the most informative predictors. Second, we address the significant class imbalance in the dataset using the Synthetic Minority Oversampling Technique (SMOTE), which substantially improves model training. Third, we propose a novel hybrid model that integrates particle swarm optimization (PSO) with an artificial neural network (ANN) to optimize feature weighting and bias training. Additionally, we conduct a comparative analysis with traditional classifiers, including Logistic Regression and Random Forest, using the National Health and Nutritional Examination Survey dataset. Our results demonstrate that while conventional classifiers achieve an accuracy of 95.8%, the proposed PSO-ANN model attains an enhanced accuracy of up to 97% in predicting CHD. This work clearly defines its contributions by improving feature selection, handling data imbalance, and introducing an innovative hybrid model for superior prediction performance.
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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.