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Decision tree classifiers for automated medical diagnosis
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“Physicians frequently use decision trees as tools; however, they are effectively tied to the initial tree structure and thus somewhat static (8).”
From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [8]
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Yassin, N. I. R., Omran, S., Houby, E. M. F. E., & Allam, H. (2017) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.12.012 · Cited by 399
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Accessing Artificial Intelligence for Clinical Decision-Making
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Metadata
- Authors
- Azar, A. T., & El-Metwally, S. M.
- Affiliations
- Misr University for Science and Technology (EG)
- Cairo University (EG)
- Corresponding author
- Azar, Ahmad Taher
- Venue
- Neural Computing and Applications
- Published
- 2012-10-07
- Volume
- 23
- Issue
- 7-8
- Pages
- 2387-2403
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W1982067074
- Cited by
- 242
- References
- 86
- Citations by year
- 2026: 9 · 2025: 29 · 2024: 29 · 2023: 27 · 2022: 26 · 2021: 24 · 2020: 28 · 2019: 18 · 2018: 15 · 2017: 19 · 2016: 8 · 2015: 6 · 2014: 3 · 2013: 1
- FWCI
- 5.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Neural Computing and Applications (publishedVersion)
- Funders
- National Cancer Institute
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- breast cancer detection, decision tree classification, boosted decision trees, decision tree forest, clinical decision support systems, automated medical diagnosis, Wisconsin Breast Cancer dataset, classifier comparison
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