ArticleDiagnostics (Basel, Switzerland)2024
An Improved Long Short-Term Memory Algorithm for Cardiovascular Disease Prediction.
Article in Diagnostics (Basel, Switzerland), 2024. 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.
- Predicting ventricular arrhythmia in myocardial ischemia using deep learning.Heart rhythm O2 · 2026Article
- Malnutrition Severity Drives Mortality in Geriatric Heart Failure: A Multicenter Extreme Gradient Boosting Analysis.Reviews in cardiovascular medicine · 2026Article
- A long short-term memory network with SHAP interpretability for dynamic prediction of ICU delirium: development and external validation.Frontiers in digital health · 2026Article
- Improved Inception-Capsule deep learning model with enhanced feature selection for early prediction of heart disease.Scientific reports · 2025Article
- Novel Tunable Pseudoresistor-Based Chopper-Stabilized Capacitively Coupled Amplifier and Its Machine Learning-Based Application.Micromachines · 2025Article
- SRADHO: statistical reduction approach with deep hyper optimization for disease classification using artificial intelligence.Scientific reports · 2025Article
- Enhanced cardiovascular disease prediction through self-improved Aquila optimized feature selection in quantum neural network & LSTM model.Frontiers in medicine · 2024Article
Corrections and comments
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4 authors.
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Abstract
Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM's network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model's efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM's superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.
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Registered trials
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