New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection
Open accessAbstract
The number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.
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The accuracy of machine learning classifiers for heart disease diagnosis has been found to be low.
“However, the accuracy of such classifiers has been found to be low.”
From A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS (2020) · cites this paper as [17]
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Flagged first, then by citations
Research works printed but not linked: 2
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Cited by
Sorted by citations
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- Authors
- Nourmohammadi-Khiarak, J., Feizi-Derakhshi, M.-R., Behrouzi, K., Mazaheri, S., Zamani-Harghalani, Y., & Tayebi, R. M.
- Affiliations
- Islamic Azad University, Tehran (IR)
- Ontario Tech University (CA)
- University of Tabriz (IR)
- Venue
- Health and Technology
- Published
- 2019-12-03
- Volume
- 10
- Issue
- 3
- Pages
- 667-678
- ISSN
- 2190-7188 (ISSN-L)
- 2190-7196
- OpenAlex
- W2991899257
- Cited by
- 109
- References
- 26
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 25 · 2023: 15 · 2022: 25 · 2021: 15 · 2020: 8
- FWCI
- 13.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Health and Technology (publishedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- heart disease diagnosis, feature selection, imperialist competitive algorithm, metaheuristic optimization, k-nearest neighbors, imbalanced medical data
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