ArticleBioMed research international2020
Improving an Intelligent Detection System for Coronary Heart Disease Using a Two-Tier Classifier Ensemble.
Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Predicting mortality risk in hospitalized ACS patients with hypertensive comorbidity: an interpretable machine learning approach.BMC medical informatics and decision making · 2026Article
- Time series electrocardiography (ECG) data for early prediction of cardiac arrest.Scientific reports · 2026Article
- A hybrid stacked autoencoder and support vector machines-based expert system for heart failure detection.Scientific reports · 2026Article
- Padding interpolation, median imputation, RobustScalar, and particle swarm optimization with heterogeneous classifiers: a robust combination for effective heart disease diagnosis.Frontiers in medicine · 2025Article
- Explainable Transfer Learning with Residual Attention BiLSTM for Prognosis of Ischemic Heart Disease.F1000Research · 2025Article
- Hematoma expansion prediction based on SMOTE and XGBoost algorithm.BMC medical informatics and decision making · 2024Article
- Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).Scientific reports · 2024Article
- Article
- Heart failure patients monitoring using IoT-based remote monitoring system.Scientific reports · 2023Article
- Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- Machine learning improves risk stratification of coronary heart disease and stroke.Annals of translational medicine · 2022Article
- IoT Based Smart Monitoring of Patients' with Acute Heart Failure.Sensors (Basel, Switzerland) · 2022Article
- Design and Development of Modified Ensemble Learning with Weighted RBM Features for Enhanced Multi-disease Prediction Model.New generation computing · 2022Article
- E-CatBoost: An efficient machine learning framework for predicting ICU mortality using the eICU Collaborative Research Database.PloS one · 2022Article
- A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques.Journal of healthcare engineering · 2022Article
- Efficient Model for Coronary Artery Disease Diagnosis: A Comparative Study of Several Machine Learning Algorithms.Journal of healthcare engineering · 2022Article
- Machine Learning Technology-Based Heart Disease Detection Models.Journal of healthcare engineering · 2022Article
- Review
- Comparison of machine learning algorithms applied to symptoms to determine infectious causes of death in children: national survey of 18,000 verbal autopsies in the Million Death Study in India.BMC public health · 2021Article
- Research on an Identification Method for Gas Disaster Risk Based on the Selective Ensemble Classification Model.ACS omega · 2021Article
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Authors and funding
3 authors.
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Abstract
Coronary heart disease (CHD) is one of the severe health issues and is one of the most common types of heart diseases. It is the most frequent cause of mortality across the globe due to the lack of a healthy lifestyle. Owing to the fact that a heart attack occurs without any apparent symptoms, an intelligent detection method is inescapable. In this article, a new CHD detection method based on a machine learning technique, e.g., classifier ensembles, is dealt with. A two-tier ensemble is built, where some ensemble classifiers are exploited as base classifiers of another ensemble. A stacked architecture is designed to blend the class label prediction of three ensemble learners, i.e., random forest, gradient boosting machine, and extreme gradient boosting. The detection model is evaluated on multiple heart disease datasets, i.e., Z-Alizadeh Sani, Statlog, Cleveland, and Hungarian, corroborating the generalisability of the proposed model. A particle swarm optimization-based feature selection is carried out to choose the most significant feature set for each dataset. Finally, a two-fold statistical test is adopted to justify the hypothesis, demonstrating that the performance differences of classifiers do not rely upon an assumption. Our proposed method outperforms any base classifiers in the ensemble with respect to 10-fold cross validation. Our detection model has performed better than current existing models based on traditional classifier ensembles and individual classifiers in terms of accuracy,
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