ArticleDiagnostics (Basel, Switzerland)2022
Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 91 citations in OpenAlex.
- Enhanced Prediction of Cardiac Risk in Neonates Using Calibrated Ensemble Learning Approaches.Pediatric cardiology · 2026Article
- Grey wolf optimized neural network with hybrid feature extraction for heart disease prediction.Scientific reports · 2026Article
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- An Interpretable Fuzzy Distance-Based Ensemble Framework with SHAP Analysis for Clinically Transparent Prediction of Diabetes.Diagnostics (Basel, Switzerland) · 2026Article
- Uncertainty-aware feature-weighted ensemble framework for heart disease prediction.Scientific reports · 2026Article
- A hybrid stacked ensemble learning framework for multilabel text emotion detection.Scientific reports · 2026Article
- Sustainable and interpretable heart disease prediction: a clinical decision support approach for biomedical healthcare applications.Scientific reports · 2026Article
- A hybrid CNN-XLSTM-GRU deep learning model with autoencoder-based feature selection for hypothyroidism diagnosis.PloS one · 2026Article
- Predictive value ofFrontiers in oncology · 2026Article
- Deep learning for cardiovascular disease: a comprehensive review of detection and risk forecasting.Frontiers in artificial intelligence · 2026Review
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence.Frontiers in artificial intelligence · 2025Article
- Federated learning-based multimodal approach for early detection and personalized care in cardiac disease.Frontiers in physiology · 2025Article
- Deep learning for cardiovascular management: optimizing pathways and cost control under diagnosis-related group models.Frontiers in artificial intelligence · 2025Review
- A comprehensive health assessment approach using ensemble deep learning model for remote patient monitoring with IoT.Scientific reports · 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
- Editorial on Special Issue "Medical Data Processing and Analysis".Diagnostics (Basel, Switzerland) · 2023Article
- Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
Abstract
Many epidemics have afflicted humanity throughout history, claiming many lives. It has been noted in our time that heart disease is one of the deadliest diseases that humanity has confronted in the contemporary period. The proliferation of poor habits such as smoking, overeating, and lack of physical activity has contributed to the rise in heart disease. The killing feature of heart disease, which has earned it the moniker the "silent killer," is that it frequently has no apparent signs in advance. As a result, research is required to develop a promising model for the early identification of heart disease using simple data and symptoms. The paper's aim is to propose a deep stacking ensemble model to enhance the performance of the prediction of heart disease. The proposed ensemble model integrates two optimized and pre-trained hybrid deep learning models with the Support Vector Machine (SVM) as the meta-learner model. The first hybrid model is Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) (CNN-LSTM), which integrates CNN and LSTM. The second hybrid model is CNN-GRU, which integrates CNN with a Gated Recurrent Unit (GRU). Recursive Feature Elimination (RFE) is also used for the feature selection optimization process. The proposed model has been optimized and tested using two different heart disease datasets. The proposed ensemble is compared with five machine learning models including Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (K-NN), Decision Tree (DT), Naïve Bayes (NB), and hybrid models. In addition, optimization techniques are used to optimize ML, DL, and the proposed models. The results obtained by the proposed model achieved the highest performance using the full feature set.
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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.