A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data

By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from rea...

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Detalles Bibliográficos
Autores principales: Hajjej, Fahima, Alohali, Manal Abdullah, Badr, Malek, Rahman, Md Adnan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283053/
https://www.ncbi.nlm.nih.gov/pubmed/35845927
http://dx.doi.org/10.1155/2022/9449497
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author Hajjej, Fahima
Alohali, Manal Abdullah
Badr, Malek
Rahman, Md Adnan
author_facet Hajjej, Fahima
Alohali, Manal Abdullah
Badr, Malek
Rahman, Md Adnan
author_sort Hajjej, Fahima
collection PubMed
description By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision.
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spelling pubmed-92830532022-07-15 A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data Hajjej, Fahima Alohali, Manal Abdullah Badr, Malek Rahman, Md Adnan Biomed Res Int Research Article By comparing the performance of various tree algorithms, we can determine which one is most useful for analyzing biomedical data. In artificial intelligence, decision trees are a classification model known for their visual aid in making decisions. WEKA software will evaluate biological data from real patients to see how well the decision tree classification algorithm performs. Another goal of this comparison is to assess whether or not decision trees can serve as an effective tool for medical diagnosis in general. In doing so, we will be able to see which algorithms are the most efficient and appropriate to use when delving into this data and arrive at an informed decision. Hindawi 2022-07-07 /pmc/articles/PMC9283053/ /pubmed/35845927 http://dx.doi.org/10.1155/2022/9449497 Text en Copyright © 2022 Fahima Hajjej et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Hajjej, Fahima
Alohali, Manal Abdullah
Badr, Malek
Rahman, Md Adnan
A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title_full A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title_fullStr A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title_full_unstemmed A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title_short A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data
title_sort comparison of decision tree algorithms in the assessment of biomedical data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9283053/
https://www.ncbi.nlm.nih.gov/pubmed/35845927
http://dx.doi.org/10.1155/2022/9449497
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