ArticleComputational intelligence and neuroscience2022
Cardiovascular Disease Detection using Ensemble Learning.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Stacking Ensemble Learning for Diagnosing Acute Myocardial Infarction in Mice Using Plasma Raman Spectroscopy and Mass Spectrometry.Journal of cardiovascular translational research · 2026Article
- Per- and Polyfluoroalkyl Substances Exposure and Ischemic Heart Disease: Emerging Evidence from the Literature.Antioxidants (Basel, Switzerland) · 2026Review
- Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach.Healthcare informatics research · 2026Article
- MemAF-KTCBM: a Memory-Aware Fractional optimisation-enabled deep learning model for atherosclerosis classification.Frontiers in medical technology · 2026Article
- Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.Scientific reports · 2025Article
- Long-term Major Adverse Cardiac Event Prediction by Computed Tomography-derived Plaque Measures and Clinical Parameters Using Machine Learning.Internal medicine (Tokyo, Japan) · 2025Article
- Federated learning-based multimodal approach for early detection and personalized care in cardiac disease.Frontiers in physiology · 2025Article
- Triboostcardio ensemble model for cardiovascular disease detection using advanced blockchain-enabled health monitoring.Frontiers in artificial intelligence · 2025Article
- Antihypertensive Drug Recommendations for Reducing Arterial Stiffness in Patients With Hypertension: Machine Learning-Based Multicohort (RIGIPREV) Study.Journal of medical Internet research · 2024Article
- Ensemble machine learning for predicting in-hospital mortality in Asian women with ST-elevation myocardial infarction (STEMI).Scientific reports · 2024Article
- Ensemble Learning for Disease Prediction: A Review.Healthcare (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular disease as earlier as possible. Many individuals worldwide die each year from cardiovascular disease. Since heart disease is a major concern, it must be dealt with timely. Multiple variables affecting health, such as excessive blood pressure, elevated cholesterol, an irregular pulse rate, and many more, make it challenging to diagnose cardiac disease. Thus, artificial intelligence can be useful in identifying and treating diseases early on. This paper proposes an ensemble-based approach that uses machine learning (ML) and deep learning (DL) models to predict a person's likelihood of developing cardiovascular disease. We employ six classification algorithms to predict cardiovascular disease. Models are trained using a publicly available dataset of cardiovascular disease cases. We use random forest (RF) to extract important cardiovascular disease features. The experiment results demonstrate that the ML ensemble model achieves the best disease prediction accuracy of 88.70%.
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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.