liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
An approach for generating fuzzy rules from decision trees
Linköping University, The Institute of Technology. Linköping University, Department of Biomedical Engineering, Medical Informatics.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.ORCID iD: 0000-0001-6468-2432
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology.
Show others and affiliations
2006 (English)In: Ubiquity: Technologies for Better Health in Aging Societies - Proceedings of MIE2006 / [ed] Arie Hasman, Reinhold Haux, Johan van der Lei, Etienne De Clercq, Francis Roger-France, IOS Press , 2006, 581-586 p.Conference paper, Published paper (Refereed)
Abstract [en]

Identifying high-risk breast cancer patients is vital both for clinicians and for patients. Some variables for identifying these patients such as tumor size are good candidates for fuzzification. In this study, Decision Tree Induction (DTI) has been applied to 3949 female breast cancer patients and crisp If-Then rules has been acquired from the resulting tree. After assigning membership functions for each variable in the crisp rules, they were converted into fuzzy rules and a mathematical model was constructed. One hundred randomly selected cases were examined by this model and compared with crisp rules predictions. The outcomes were examined by the area under the ROC curve (AUC). No significant difference was noticed between these two approaches for prediction of recurrence of breast cancer. By soft discretization of variables according to resulting rules from DTI, a predictive model, which is both more robust to noise and more comprehensible for clinicians, can be built.

Place, publisher, year, edition, pages
IOS Press , 2006. 581-586 p.
Series
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 124
Keyword [en]
Fuzzy Set Theory, Decision Tree Induction, Breast Cancer, Distant Metastasis
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-36666ISI: 000281143200082PubMedID: 17108580Local ID: 32105ISBN: 978-1-58603-647-8 (print)OAI: oai:DiVA.org:liu-36666DiVA: diva2:257515
Conference
The XXst International Congress of the European Federation for Medical Informatics
Available from: 2009-10-10 Created: 2009-10-10 Last updated: 2013-05-15

Open Access in DiVA

No full text

Other links

PubMedhttp://ebooks.iospress.nl/publication/9748

Authority records BETA

Razavi, Amir RezaNyström, MikaelStachowicz, Marian S.Gill, HansÅhlfeldt, HansShahsavar, Nosrat

Search in DiVA

By author/editor
Razavi, Amir RezaNyström, MikaelStachowicz, Marian S.Gill, HansÅhlfeldt, HansShahsavar, Nosrat
By organisation
The Institute of TechnologyMedical Informatics
Medical and Health Sciences

Search outside of DiVA

GoogleGoogle Scholar

pubmed
isbn
urn-nbn

Altmetric score

pubmed
isbn
urn-nbn
Total: 518 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf