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A Comparison of Decision Tree Algorithms in the Assessment of Biomedical Data.
Hajjej, Fahima; Alohali, Manal Abdullah; Badr, Malek; Rahman, Md Adnan.
Afiliação
  • Hajjej F; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
  • Alohali MA; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.
  • Badr M; Department of Medical Instruments Engineering Techniques, Al-Farahidi University, Baghdad 10021, Iraq.
  • Rahman MA; Research Center, The University of Mashreq, Baghdad, Iraq.
Biomed Res Int ; 2022: 9449497, 2022.
Article em En | MEDLINE | ID: mdl-35845927
ABSTRACT
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.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Inteligência Artificial Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Algoritmos / Inteligência Artificial Tipo de estudo: Health_economic_evaluation / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article