Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks
Open accessAbstract
The World Health Organization (WHO) estimated one third of all global deaths reason as cardiovascular diseases in 2015. Some computational techniques were proposed for investigation of heart diseases. This study proposes a genetic algorithm (GA) based trained recurrent fuzzy neural networks (RFNN) to diagnosis of heart diseases. The University of California Irvine (UCI) Cleveland heart disease dataset is used in this study. Out of total 297 instances of patient data, 252 are used for training and 45 of them are chosen to be the testing. The results showed that 97.78% accuracy was obtained from testing set. In addition to the accuracy, root mean square error, the probability of the misclassification error, specificity, sensitivity, precision and F-score are calculated. The results were found to be satisfying based on comparison.
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The crow search algorithm does not guarantee convergence due to poor exploration of the search space.
“However, this search strategy does not guarantee convergence due to the poor exploration of the search space [10].”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [10]
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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
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- Authors
- Uyar, K., & İlhan, A.
- Affiliations
- Near East University (CY)
- Corresponding author
- Uyar, Kaan
- Venue
- Procedia Computer Science
- Publisher
- Elsevier BV
- Published
- 2017
- Volume
- 120
- Pages
- 588-593
- ISSN
- 1877-0509 (ISSN-L)
- OpenAlex
- W2774583472
- Cited by
- 208
- References
- 25
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 29 · 2023: 31 · 2022: 37 · 2021: 41 · 2020: 28 · 2019: 13 · 2018: 6
- FWCI
- 17.35
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia Computer Science (publishedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Fuzzy Logic and Control Systems
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- heart disease diagnosis, recurrent fuzzy neural network, genetic algorithm, cardiovascular disease, medical classification, fuzzy logic
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