The classifier for prediction of peri-operative complications in cervical cancer treatment

Abstract

This paper addresses the problem of creating a new classifier as highly interpretable fuzzy rule-based system, based on the analytical theory of fuzzy modeling and gene expression programming. This approach is applied to solve the prediction problem of peri-operative complications of radical hysterectomy in patients with cervical cancer. The developed classifier has the form of the set of fuzzy metarules, which are readable for the medical community, and additionally, is accurate enough. The consequents of the metarules describe the presence or absence of peri-operative complications. For the construction of the classifier we can use the fuzzified, binarized or both types of the attributes. We also compare the efficiency of our model with the decision trees and C5 algorithm. © 2014 Springer International Publishing.