ArticleJournal of healthcare engineering2022
A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- An explainable machine learning (XAI) framework to enhance types of cardiovascular disease diagnosis and prognosis.Physical and engineering sciences in medicine · 2026Article
- An efficient artificial neural network-based optimization techniques for the early prediction of coronary heart disease: comprehensive analysis.Scientific reports · 2025Article
- Developing in hospital mortality prediction model tools for patients with acute myocardial infarction (STEMI) using Yazd cardiovascular disease registry, YCDR data.ARYA atherosclerosis · 2025Article
- Explainable Transfer Learning with Residual Attention BiLSTM for Prognosis of Ischemic Heart Disease.F1000Research · 2025Article
- A voting-based machine learning approach for classifying biological and clinical datasets.BMC bioinformatics · 2023Article
- Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2022Article
- Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
4 authors.
Funding
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Abstract
Machine intelligence can convert raw clinical data into an informational source that helps make decisions and predictions. As a result, cardiovascular diseases are more likely to be addressed as early as possible before affecting the lifespan. Artificial intelligence has taken research on disease diagnosis and identification to another level. Despite several methods and models coming into existence, there is a possibility of improving the classification or forecast accuracy. By selecting the connected combination of models and features, we can improve accuracy. To achieve a better solution, we have proposed a reliable ensemble model in this paper. The proposed model produced results of 96.75% on the cardiovascular disease dataset obtained from the Mendeley Data Center, 93.39% on the comprehensive dataset collected from IEEE DataPort, and 88.24% on data collected from the Cleveland dataset. With this proposed model, we can achieve the safety and health security of an individual.
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Registered trials
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