In recent years the artificial intelligence has been developed rapidly since it can be applied easily to several areas like medical diagnosis, engineering and economics, among others. In this study we have devised a soft expert system (SES) as a prediction system for prostate cancer by using the prostate specific antigen (PSA), prostate volume (PV) and age factors of patients based on fuzzy sets and soft sets and have calculated the patients' prostate cancer risk. Our data set has been provided by the

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Bat Algorithm (BA) and Artificial Bee Colony Algorithm (ABC) are frequently used in solving global optimization problems. Many new algorithms in the literature are obtained by modifying these algorithms for both constrained and unconstrained optimization problems or using them in a hybrid manner with different algorithms. Although successful algorithms have been proposed, BA’s performance declines in complex and large-scale problems are still an ongoing problem. The inadequate global search capability of the BA resulting from its algorithm structure is the major cause of this problem. In this study, firstly, inertia weight was added to the speed formula to improve the search capability of the BA. Then, a new algorithm that operates in a hybrid manner with the ABC algorithm, whose diversity and global search capability is stronger than the BA, was proposed. The performance of the proposed algorithm (BA_ABC) was examined in four different test groups, including classic benchmark functions, CEC2005 small-scale test functions, CEC2010 large-scale test functions, and classical engineering design problems. The BA_ABC results were compared with different algorithms in the literature and current versions of the BA for each test group. The results were interpreted with the help of statistical tests. Furthermore, the contribution of BA and ABC algorithms, which constitute the hybrid algorithm, to the solutions is examined. The proposed algorithm has been found to produce successful and acceptable results.

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Rough Set Theory (RST) is a mathematical method used in reasoning and information extraction for expert systems. RST makes incomplete, inadequate or ambiguous information appropriate for data analysis by editing it. Today, incomplete data are found in many datasets. Extracting rules from these incomplete data, which are frequently found in disease data, is extremely important in the diagnosis of diseases. In this study, an algorithm previously proposed and extracting fuzzy rules from datasets containing only "do not care" missing attribute value by RST was developed in a way that it can extract fuzzy rules from datasets containing missing attribute value in both "do not care" and "lost" type. The algorithm developed was applied to the dataset of thyroid disease and certain and possible fuzzy rules were obtained for the diagnosis of the disease. The performance of the algorithm was investigated on six different datasets that had "do not care" and "lost" kinds of missing attribute values in different numbers. It was found that the algorithm generally produced successful and consistent rules in the datasets that had "do not care" and "lost" missing attribute values.