Cited by 736Artificial Intelligence in HealthcareData Mining Algorithms and ApplicationsData Mining and Machine Learning Applications

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Decision Trees: An Overview and Their Use in Medicine

Podgorelec, Vili, Kokol, Peter, Stiglic, Bruno, Rozman, Ivan·2002·Journal of Medical Systems

DOI 10.1023/a:1016409317640

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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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A Mathematical Theory of Communication
Shannon, C. E. (1948) · The MIT Press eBooks · doi:10.1002/j.1538-7305.1948.tb00917.x · Cited by 50,201
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
Untitled work
(1992) · Cited by 23,431
Induction of decision trees
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
Genetic Algorithms in Search
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 9,882
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

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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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Authors: Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I.
Affiliations: University of Maribor (SI)
Corresponding author: Podgorelec, Vili
ORCID: Podgorelec, Vili
Kokol, Peter
Venue: Journal of Medical Systems
Publisher: Springer Science and Business Media LLC
Published: 2002-10
Volume: 26
Issue: 5
Pages: 445-463
ISSN: 0148-5598 (ISSN-L)
1573-689X
DOI: 10.1023/a:1016409317640
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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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Decision Trees: An Overview and Their Use in Medicine

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