ArticleEuropean journal of medical research2024
Cardiovascular disease diagnosis: a holistic approach using the integration of machine learning and deep learning models.
Article in European journal of medical research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Learning Periodic Patterns in ECG Signals Using TimesNet for Automated Cardiac Classification.Biomedicines · 2026Article
- Letter to the Editor: Rethinking the clinical utility of machine learning for CTEPH severity.European radiology · 2026Article
- A hybrid feature selection framework combining Artificial Bee Colony and decision trees for CVD risk assessment.Scientific reports · 2026Article
- Multimodal deep learning with missing data robustness for enhanced early diagnosis of coronary artery disease using CCTA, clinical, and ECG data.BMC medical informatics and decision making · 2026Article
- Comparative evaluation of deep learning models for cardiovascular disease diagnosis and classification.Scientific reports · 2026Article
- A preprocessing-enhanced stacking classifier for generalized cardiovascular disease detection across diverse datasets.Scientific reports · 2026Article
- An explainable machine learning (XAI) framework to enhance types of cardiovascular disease diagnosis and prognosis.Physical and engineering sciences in medicine · 2026Article
- Scientific Publication Trend: Using Advanced Technologies in Cardiovascular Disease Research in theJournal of cardiovascular development and disease · 2026Article
- A composite model incorporating systemic inflammatory burden and GRACE score for enhanced identification of elevated myocardial stress in acute myocardial infarction: an exploratory analysis.Frontiers in cardiovascular medicine · 2026Article
- Do nutritional variables improve cardiovascular disease prediction? A comparative machine learning analysis.Frontiers in nutrition · 2026Article
- A hybrid deep learning and large language model framework for MACE risk prediction and evidence-based clinical recommendations.Frontiers in cardiovascular medicine · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Cardiology-Chat: A Multi-LLMs Powered System for Cardiac Diagnostic Reasoning and Clinical Support.IEEE journal of translational engineering in health and medicine · 2026Article
- Comparative performance of machine learning and deep learning models for heart disease prediction in a small clinical dataset.American journal of cardiovascular disease · 2026Article
- Developing a Data Trust Model (Not Only) for Sleep Research: Conceptual Study and Quantitative Survey.JMIR human factors · 2025Article
- Improving emotional connection of human and machine using Deep Maxout Networks optimized through Modified Water Cycle optimizer.Scientific reports · 2025Article
- Fraud detection and explanation in medical claims using GNN architectures.Scientific reports · 2025Article
- RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- Explainable attention-based deep learning for classification and interpretation of heart murmurs using phonocardiograms.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe incidence and mortality rates of cardiovascular disease worldwide are a major concern in the healthcare industry. Precise prediction of cardiovascular disease is essential, and the use of machine learning and deep learning can aid in decision-making and enhance predictive abilities.
objectiveThe goal of this paper is to introduce a model for precise cardiovascular disease prediction by combining machine learning and deep learning.
methodTwo public heart disease classification datasets with 70,000 and 1190 records besides a locally collected dataset with 600 records were used in our experiments. Then, a model which makes use of both machine learning and deep learning was proposed in this paper. The proposed model employed CNN and LSTM, as the representatives of deep learning models, besides KNN and XGB, as the representatives of machine learning models. As each classifier defined the output classes, majority voting was then used as an ensemble learner to predict the final output class.
resultThe proposed model obtained the highest classification performance based on all evaluation metrics on all datasets, demonstrating its suitability and reliability in forecasting the probability of cardiovascular disease.
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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.