The DOI matches its Crossref record; 52 of the 54 references it cites were found in a registry or an index.
Association rule mining to detect factors which contribute to heart disease in males and females
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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
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“CNN is able to generate features with various positions in the testing phase of the patient [22], [41].”
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Metadata
- Authors
- Nahar, J., Imam, T., Tickle, K. S., & Chen, Y.-P. P.
- Affiliations
- Central Queensland University (AU)
- La Trobe University (AU)
- Corresponding author
- Nahar, Jesmin
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2013-03
- Volume
- 40
- Issue
- 4
- Pages
- 1086-1093
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2080084655
- Cited by
- 394
- References
- 54
- Citations by year
- 2026: 11 · 2025: 20 · 2024: 31 · 2023: 27 · 2022: 43 · 2021: 50 · 2020: 39 · 2019: 42 · 2018: 39 · 2017: 26 · 2016: 26 · 2015: 18 · 2014: 14 · 2013: 7
- FWCI
- 20.79
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- heart disease, coronary artery disease, association rule mining, gender differences, risk factors, Cleveland heart disease dataset, Apriori algorithm, chest pain, exercise-induced angina, resting ECG, heart disease prediction
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