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An approach for generating fuzzy rules from decision trees
Linköpings universitet, Tekniska högskolan. Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0001-6468-2432
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
Linköpings universitet, Institutionen för medicinsk teknik, Medicinsk informatik. Linköpings universitet, Tekniska högskolan.
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2006 (Engelska)Ingår i: 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, s. 581-586Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Ort, förlag, år, upplaga, sidor
IOS Press , 2006. s. 581-586
Serie
Studies in Health Technology and Informatics, ISSN 0926-9630 ; 124
Nyckelord [en]
Fuzzy Set Theory, Decision Tree Induction, Breast Cancer, Distant Metastasis
Nationell ämneskategori
Medicin och hälsovetenskap
Identifikatorer
URN: urn:nbn:se:liu:diva-36666ISI: 000281143200082PubMedID: 17108580Lokalt ID: 32105ISBN: 978-1-58603-647-8 (tryckt)OAI: oai:DiVA.org:liu-36666DiVA, id: diva2:257515
Konferens
The XXst International Congress of the European Federation for Medical Informatics
Tillgänglig från: 2009-10-10 Skapad: 2009-10-10 Senast uppdaterad: 2022-07-06

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

Person

Razavi, Amir RezaNyström, MikaelGill, HansÅhlfeldt, HansShahsavar, Nosrat

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Razavi, Amir RezaNyström, MikaelGill, HansÅhlfeldt, HansShahsavar, Nosrat
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Tekniska högskolanMedicinsk informatik
Medicin och hälsovetenskap

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