wFDT - Weighted Fuzzy Decision Trees for Prognosis of Breast Cancer Survivability

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 consider...

• Type: Conference
• Year: 2008
• Where: AUSDM
• Authors: Umer Khan, Hyunjung Shin, Jongpill Choi, Minkoo Kim
• Keywords: AUSDM 2008, Breast Cancer, Data Mining, Decision Tree, Fuzzy Decision Trees