Fuzzy expert system for predicting pathological stage of prostate cancer

• Published in: Expert systems with applications 2013-02, Vol.40 (2), p.466-470
• Main Authors: Castanho, M.J.P., Hernandes, F., De Ré, A.M., Rautenberg, S., Billis, A.
• Format: Article
• Language: English
• Subjects: Animal tumors. Experimental tumors; Applied sciences; Artificial intelligence; Biological and medical sciences; Cancer; Classification; Computer science; control theory; systems; Exact sciences and technology; Experimental genital and mammary tumors; Expert systems; Fuzzy; Fuzzy logic; Fuzzy rule-based system; Fuzzy set theory; Genetic algorithm; Medical sciences; Prostate; Prostate cancer; Radicals; Tumors
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2012.07.046

► 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. 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.