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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]
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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