ArticleDiagnostics (Basel, Switzerland)2023
Prediction of Heart Disease Based on Machine Learning Using Jellyfish Optimization Algorithm.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 77 citations in OpenAlex.
- A comparative study of quantum-inspired PSO and EA with their binary variants for heart disease classification.Scientific reports · 2026Article
- CardioMetaHybridOptimizer as a behaviorally adaptive multi-phase metaheuristic framework for interpretable cardiovascular disease diagnosis.BMC bioinformatics · 2026Article
- Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability Analysis.Biomedical engineering and computational biology · 2026Article
- Harris Hawks optimization based deep learning models for heart disease diagnosis.Scientific reports · 2025Article
- A new ensemble heart attack diagnosis (EHAD) model using artificial intelligence techniques.Scientific reports · 2025Article
- Dementia and Heart Failure Classification Using Optimized Weighted Objective Distance and Blood Biomarker-Based Features.Bioengineering (Basel, Switzerland) · 2025Article
- SGO enhanced random forest and extreme gradient boosting framework for heart disease prediction.Scientific reports · 2025Article
- HFSA: hybrid feature selection approach to improve medical diagnostic system.PeerJ. Computer science · 2025Article
- Transfer learning-based hybrid VGG16-machine learning approach for heart disease detection with explainable artificial intelligence.Frontiers in artificial intelligence · 2025Article
- Integrating EPSOSA-BP neural network algorithm for enhanced accuracy and robustness in optimizing coronary artery disease prediction.Scientific reports · 2024Article
- Article
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
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Authors and funding
2 authors at 2 institutions in 2 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Heart disease is one of the most known and deadly diseases in the world, and many people lose their lives from this disease every year. Early detection of this disease is vital to save people's lives. Machine Learning (ML), an artificial intelligence technology, is one of the most convenient, fastest, and low-cost ways to detect disease. In this study, we aim to obtain an ML model that can predict heart disease with the highest possible performance using the Cleveland heart disease dataset. The features in the dataset used to train the model and the selection of the ML algorithm have a significant impact on the performance of the model. To avoid overfitting (due to the curse of dimensionality) due to the large number of features in the Cleveland dataset, the dataset was reduced to a lower dimensional subspace using the Jellyfish optimization algorithm. The Jellyfish algorithm has a high convergence speed and is flexible to find the best features. The models obtained by training the feature-selected dataset with different ML algorithms were tested, and their performances were compared. The highest performance was obtained for the SVM classifier model trained on the dataset with the Jellyfish algorithm, with Sensitivity, Specificity, Accuracy, and Area Under Curve of 98.56%, 98.37%, 98.47%, and 94.48%, respectively. The results show that the combination of the Jellyfish optimization algorithm and SVM classifier has the highest performance for use in heart disease prediction.
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