Goal of this study is a design of a fuzzy expert system, its application aspects in the medicine area and its introduction for calculation of numeric value of prostate cancer risk. For this aim it was used prostate specific antigen (PSA), age and prostate volume (PV) as system input parameters and prostate cancer risk (PCR) as output. This system gives user a range of the risk of the cancer disease and facilitates the decision of the doctor if there is a need for the biopsy. The designed system was tested by the data from the literature and the clinical data. It was compared the diagnoses data of specialists of the every disease situation and literature data and it was seen that the system can be available for every situation. It is observed that this system is rapid because it needs minimum calculation, economical, without any risk than traditional diagnostic systems, has also a high reliability than the other system and can be used as assistant system for physicians. Having used in the hospital this system was tested as decision support system and the approach used in this study can be used in difference studies and analyses, because the system is transparent and explainable to a user. his is the abstract section.

Particle Swarm Optimization (PSO) algorithm inspired from behaviour of bird flocking and fish schooling. It is well-known algorithm which has been used in many areas successfully. However it sometimes suffers from premature convergence. In resent year’s researches have been introduced a various approaches to avoid of this problem. This paper presents the particle swarm optimization algorithm with flexible swarm (PSO-FS). The new algorithm was evaluated on 14 functions often used to benchmark the performance of optimization algorithms. PSO-FS algorithm was compared to some other modifications of PSO. The results show that PSO-FS always performed one of the better results.