Fuzzy expert system for predicting pathological stage of prostate cancer

Prostate cancer is the second most common cancer among men, responsible for the loss of half a million lives each year worldwide, according to the World Health Organization. In prostate cancer, definitive therapy such as radical prostatectomy, is more effective when the cancer is organ-confined. The aim of this study is to investigate the performance of some fuzzy expert systems in the classification of patients with confined or non-confined cancer. To deal with the intrinsic uncertainty about the variables utilized to predict cancer stage, the developed approach is based on Fuzzy Set Theory. A fuzzy expert system was developed with the fuzzy rules and membership functions tuned by a genetic algorithm. As a result, the utilized approach reached better precision taking into account some correlated studies.

► We model a system for predicting pathological stage of prostate cancer. ► We develop a hybrid system: a rule-based fuzzy system where a genetic algorithm is used to optimize the parameters. ► Performance of genetic-fuzzy system constructed, for the database used, show superior Partin tables.