ArticleFrontiers in medicine2023
Cardiovascular diseases prediction by machine learning incorporation with deep learning.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 125 citations in OpenAlex.
- Artificial Intelligence for Cardiovascular Risk Prediction: An Umbrella Review of Applications and Translational Challenges.Vascular health and risk management · 2026Pooled it
- Review
- Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.Tropical medicine & international health : TM & IH · 2026Article
- Leakage-Safe Precision-Aware Dual-Branch FT-Transformer for Population-Scale Heart Disease Risk Prediction.Sensors (Basel, Switzerland) · 2026Article
- Machine-learning-based cardiovascular mortality prediction using a cumulative PMScientific reports · 2026Observational
- Importance of balanced datasets with feature selection and ensemble methods on heart disease classification using distinctive machine learning techniques: a comparative analysis.Scientific reports · 2026Article
- Advancing cardiovascular disease diagnosis with an interpretable and responsible AI framework.Scientific reports · 2026Article
- Cancer treatment-related cardiotoxicity, molecular mechanism, challenges, and research trends.Discover oncology · 2026Review
- Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation.Frontiers in medicine · 2026Article
- Special Issue "New Trends in Diabetes, Hypertension and Cardiovascular Diseases: 3rd Edition".International journal of molecular sciences · 2025Article
- Integrating Large-Scale Data Analytics for Cardiovascular Disease Prediction: A Scoping Review.Healthcare informatics research · 2025Article
- A deep learning approach for heart disease detection using a modified multiclass attention mechanism with BiLSTM.Scientific reports · 2025Article
- Prediction of cardiovascular diseases based on GBDT+LR.Scientific reports · 2025Article
- Predicting cardiovascular risk with hybrid ensemble learning and explainable AI.Scientific reports · 2025Article
- Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets.Scientific reports · 2025Article
- Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy.Scientific reports · 2025Article
- Interpretable machine learning for cardiovascular risk prediction: Insights from NHANES dietary and health data.PloS one · 2025Article
- Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence.Frontiers in artificial intelligence · 2025Article
- Machine learning-based detection of cognitive decline using SSWTRT: classification performance and decision analysis.Frontiers in artificial intelligence · 2025Article
- PEDI: Towards Efficient Pathway Enrichment and Data Integration in Bioinformatics for Healthcare Using Deep Learning Optimisation.Biomedical engineering and computational biology · 2025Article
Corrections and comments
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Authors and funding
11 authors at 8 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
It is yet unknown what causes cardiovascular disease (CVD), but we do know that it is associated with a high risk of death, as well as severe morbidity and disability. There is an urgent need for AI-based technologies that are able to promptly and reliably predict the future outcomes of individuals who have cardiovascular disease. The Internet of Things (IoT) is serving as a driving force behind the development of CVD prediction. In order to analyse and make predictions based on the data that IoT devices receive, machine learning (ML) is used. Traditional machine learning algorithms are unable to take differences in the data into account and have a low level of accuracy in their model predictions. This research presents a collection of machine learning models that can be used to address this problem. These models take into account the data observation mechanisms and training procedures of a number of different algorithms. In order to verify the efficacy of our strategy, we combined the Heart Dataset with other classification models. The proposed method provides nearly 96 percent of accuracy result than other existing methods and the complete analysis over several metrics has been analysed and provided. Research in the field of deep learning will benefit from additional data from a large number of medical institutions, which may be used for the development of artificial neural network structures.
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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.