An approach for generating fuzzy rules from decision trees
Razavi, A.R.; Nyström, M.; Stachowicz, M.S.; Gill, H.; Ahlfeldt, H.; Shahsavar, N.
Studies in Health Technology and Informatics 124: 581-586
2006
ISSN/ISBN: 0926-9630 PMID: 17108580 Document Number: 595837
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.