The DOI matches its Crossref record; 42 of the 44 references it cites were found in a registry or an index.
Decision Trees: An Overview and Their Use in Medicine
Abstract
In medical decision making (classification, diagnosing, etc.) there are many situations where decision must be made effectively and reliably. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision trees are a reliable and effective decision making technique that provide high classification accuracy with a simple representation of gathered knowledge and they have been used in different areas of medical decision making. In the paper we present the basic characteristics of decision trees and the successful alternatives to the traditional induction approach with the emphasis on existing and possible future applications in medicine.
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“Decision tree is a reliable technique to use in different areas of medical domain in order to make a right decision [76,77].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [76]
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,387
Optimization by Simulated Annealing
Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983) · Science · doi:10.1126/science.220.4598.671 · Cited by 45,134
Classification and Regression Trees.
Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867
(1992) · Cited by 23,431
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Classification and Regression Trees.
Ryzin, J. V., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1986) · Journal of the American Statistical Association · doi:10.2307/2288003 · Cited by 21,030
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Goldberg, D. E. (1989) · Medical Entomology and Zoology · Cited by 10,109
The Mathematical Theory of Communication
Shannon, C. E., Weaver, W., & Wiener, N. (1950) · Physics Today · doi:10.1063/1.3067010 · Cited by 9,885
The random subspace method for constructing decision forests
Ho, T. K. (1998) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.709601 · Cited by 7,005
Research works printed but not linked: 9
- Paterson, A., and Niblett, T. B., ACLS Manual, Intelligent Terminals Ltd., Edinburgh, 1982.
- Heath, D., Kasif, S., and Salzberg, S., k-DT: A multi-tree learning method. Proc. Second Int. Workshop Multistrategy Learn. pp. 138-149, 1993.
- Heath, D., Kasif, S., and Salzberg, S., Learning oblique decision trees. Proc. Thirteenth Int. Joint Conf. Artif. Intellig. (IJCAI-93) pp. 1002-1007, 1993.
- Rich, E., and Knight, K., Artificial Intelligence (2nd edn.), McGraw Hill, New York, 1991.
- Podgorelec, V., and Kokol, P., Evolutionary decision forests-decision making with multiple evolutionary constructed decision trees, Problems in Applied Mathematics and Computational Intelligence, pp. 97-103, WSES Press, 2001.
- Zorman, M., Kokol, P., and Podgorelec, V., Medical decision making supported by hybrid decision trees. Proc. ICSC Symp. Intellig. Syst. Appl. (ISA-2000) ICSC Academic Press, 2000.
- Podgorelec, V., and Kokol, P., Induction f medical decision trees with genetic algorithms. Proc. Int. ICSC Congr. Comput. Intellig. Methods Appl. (CIMA 1999) 1999.
- Podgorelec, V., Intelligent Systems Design and Knowledge Discovery With Automatic Programming, PhD thesis, University of Maribor, Oct. 2001.
- Dantchev, N., Therapeutic decision frees in psychiatry. Encephale-Revue De Psychiatrie Clinique Biologique Et Therapeutique 22(3):205-214, 1996.
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Sorted by citations
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., Shilton, A., Yearwood, J., Dimitrova, N., Ho, T. B., et al. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.5870 · Cited by 988
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery
Yang, X. Y., Wang, Y., Byrne, R., Schneider, G., & Yang, S. (2019) · Chemical Reviews · doi:10.1021/acs.chemrev.8b00728 · Cited by 1,079
eD octor: machine learning and the future of medicine
Handelman, G. S., Kok, H. K., Chandra, R. V., Razavi, A. H., Lee, M. J., & Asadi, H. (2018) · Journal of Internal Medicine · doi:10.1111/joim.12822 · Cited by 956
Lemon, S. C., Roy, J., Clark, M. A., Friedmann, P. D., & Rakowski, W. (2003) · Annals of Behavioral Medicine · doi:10.1207/s15324796abm2603_02 · Cited by 898
Khan, A. A., Chaudhari, O., & Chandra, R. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122778 · Cited by 677
Metadata
- Authors
- Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I.
- Affiliations
- University of Maribor (SI)
- Corresponding author
- Podgorelec, Vili
- Venue
- Journal of Medical Systems
- Published
- 2002-10
- Volume
- 26
- Issue
- 5
- Pages
- 445-463
- ISSN
- 0148-5598 (ISSN-L)
- 1573-689X
- OpenAlex
- W1584935767
- Cited by
- 736
- References
- 44
- Citations by year
- 2026: 39 · 2025: 83 · 2024: 76 · 2023: 80 · 2022: 62 · 2021: 64 · 2020: 48 · 2019: 46 · 2018: 36 · 2017: 26 · 2016: 21 · 2015: 29 · 2014: 25 · 2013: 24 · 2012: 20
- FWCI
- 6.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Journal of Medical Systems (publishedVersion)
- PubMed (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
- decision tree, clinical decision-making, diagnosis, classification, knowledge representation, inductive learning, machine learning in medicine
- MeSH terms
- Decision Trees, Humans, United States, Clinical Medicine, Decision Support Systems, Clinical
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