###### Abstract
Of course, other factors of tumor genesis exist, but a detailed reference to all of them is beyond the scope of this paper. As a result of the aforementioned reasons and statistics, it is imperative to identify diagnostic markers for the early detection of PCa. Such markers exist, and one of the most valuable is prostate-specific antigen (PSA), which is used not only for the detection of PCa but as a post-treatment marker as well. Generally speaking, PSA can reduce prostate cancer mortality [32; 33]. Though it is a very valuable marker, it is not without limitations, such as overtreatment in a situation where PCa is indeed present but uncovers a harmless cancer, in which case, the treatment could be more dangerous than beneficial [34] (United States Preventive Serves Task Force, USPSTF). Furthermore, the safety levels of PSA that are used in regular screening are usually misleading, as the results from the prostate cancer prevention trial (PCPT) [35] show that men with PSA levels below 4 ng/mL could indeed have PCa [34]. Even more worrisome is the fact that men with a median age of 69 years and an average PSA of 1.5 ng/mL had PCa confirmed by a biopsy of the prostate gland [36]. Extending our reasoning, it is obvious that the PSA biomarker alone cannot provide a safe diagnosis; thus, we need additional markers such as PSAD, i.e., the PSA value divided by the prostatic volume, which provides excellent information regarding the necessity of biopsy and the likelihood of PCa [37]. PSAV is also necessary as it could increase the specificity of PCa screening and potentially advanced tumors [38]. Furthermore, an important marker is the PSA ratio, i.e., free PSA/total PSA. An increase in the PSA ratio was more related to benign problems, while a decreased PSA ratio was correlated to cancer [39]. Last but not least, the DRE plays an important role as a qualified and experienced doctor could discover an abnormality in the back surface of the prostate gland, which, as a result, could lead to a prostate biopsy to clarify the situation. This study aims to highlight the necessity of a more comprehensive standard screening procedure by taking into account several markers beyond PSA, which, as a result, leads to a better and safer diagnosis, always with the consent of the physician. Our target was the evaluation and efficacy of machine learning and Computational Intelligence algorithms for the classification of patients into two distinct categories, the high-risk and low-risk groups for PCa. The evaluation is based on surrogate data of the main biochemical marker PSA along with PSAV, PSAD, the PSA ratio, the DRE, and the age of the patient. Cross-validation techniques were applied on these surrogate data, by utilizing pattern classification algorithms such as Decision Trees, Random Forests, Support Vector Machines,K-Nearest Neighbors, Linear Regression, Naive Bayes, and Artificial Neural Networks. The obtained results indicated that prostate cancer cases can be classified with high accuracy. Last but not least, an easy-to-use Graphical User Interface (GUI) was implemented. The GUI is a consultancy tool that aims to assist a qualified physician, always under his strong supervision. Computer experiments on these classification algorithms highlighted the strong dependence of classification performance on the size of the training set that is used, i.e., the segment of the dataset that is utilized during the training procedure. These findings raised the issue of detecting the optimal training set in order to achieve optimal classification results. This issue was addressed by the implementation of hybrid classification algorithms, based on metaheuristic optimization methods such as the generational Genetic Algorithms (GAs) [40]. Specifically designed GAs were developed, incorporating simple classification algorithms like K-Nearest Neighbors, K-means clustering, and genetic clustering.