ArticleSensors (Basel, Switzerland)2022
Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 115 citations in OpenAlex.
- Machine Learning for Predicting Coronary Heart Disease Risk in Patients with Hypertension: An Ensemble Modeling Approach.Healthcare informatics research · 2026Article
- Machine learning and Mendelian randomization identify key lifestyle factors in coronary heart disease: A NHANES-Based study.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Blockchain enabled deep learning model with modified coati optimization for sustainable healthcare disease detection and classification.Scientific reports · 2025Article
- An explainable machine learning model for predicting chronic coronary disease and identifying valuable text features.Frontiers in cardiovascular medicine · 2025Article
- Enhancing stroke disease classification through machine learning models via a novel voting system by feature selection techniques.PloS one · 2025Article
- Improving privacy-preserving multi-faceted long short-term memory for accurate evaluation of encrypted time-series MRI images in heart disease.Scientific reports · 2024Article
- The classification algorithms to support the management of the patient with femur fracture.BMC medical research methodology · 2024Article
- An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Development of an efficient novel method for coronary artery disease prediction using machine learning and deep learning techniques.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Article
- An innovative model for predicting coronary heart disease using triglyceride-glucose index: a machine learning-based cohort study.Cardiovascular diabetology · 2023Article
- Review
- Internet of Medical Things Enabled Multimodal Framework: Deep Machine Learning for Chronic Cardiac Disease Prediction in Healthcare 5.0.Healthcare technology lettersArticle
Corrections and comments
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Authors and funding
8 authors at 7 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronary heart disease is one of the major causes of deaths around the globe. Predicating a heart disease is one of the most challenging tasks in the field of clinical data analysis. Machine learning (ML) is useful in diagnostic assistance in terms of decision making and prediction on the basis of the data produced by healthcare sector globally. We have also perceived ML techniques employed in the medical field of disease prediction. In this regard, numerous research studies have been shown on heart disease prediction using an ML classifier. In this paper, we used eleven ML classifiers to identify key features, which improved the predictability of heart disease. To introduce the prediction model, various feature combinations and well-known classification algorithms were used. We achieved 95% accuracy with gradient boosted trees and multilayer perceptron in the heart disease prediction model. The Random Forest gives a better performance level in heart disease prediction, with an accuracy level of 96%.
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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.