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 thesis 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.
