An approach for generating fuzzy rules from decision trees
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 (Refereed)
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.
, Studies in Health Technology and Informatics, ISSN 0926-9630 ; 124
Fuzzy Set Theory, Decision Tree Induction, Breast Cancer, Distant Metastasis
Medical and Health Sciences
IdentifiersURN: urn:nbn:se:liu:diva-36666ISI: 000281143200082PubMedID: 17108580Local ID: 32105ISBN: 978-1-58603-647-8OAI: oai:DiVA.org:liu-36666DiVA: diva2:257515
The XXst International Congress of the European Federation for Medical Informatics