Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
How good are fuzzy If-Then classifiers?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A proposal for improving the accuracy of linguistic modeling
IEEE Transactions on Fuzzy Systems
A survey of prediction models for breast cancer survivability
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Expert Systems with Applications: An International Journal
Robust predictive model for evaluating breast cancer survivability
Engineering Applications of Artificial Intelligence
| Hi-index | 0.00 |
Accurate and less invasive personalized predictive medicine can spare many breast cancer patients from receiving complex surgical biopsies, unnecessary adjuvant treatments and its expensive medical cost. Cancer prognosis estimates recurrence of disease and predict survival of patient; hence resulting in improved patient management. To develop such knowledge based prognostic system, this paper examines potential hybridization of accuracy and interpretability in the form of Fuzzy Logic and Decision Trees, respectively. Effect of rule weights on fuzzy decision trees is investigated to be an alternative to membership function modifications for performance optimization. Experiments were performed using different combinations of: number of decision tree rules, types of fuzzy membership functions and inference techniques for breast cancer survival analysis. SEER breast cancer data set (1973-2003), the most comprehensible source of information on cancer incidence in United States, is considered. Performance comparisons suggest that predictions of weighted fuzzy decision trees (wFDT) are more accurate and balanced, than independently applied crisp decision tree classifiers; moreover it has a potential to adapt for significant performance enhancement.