###### Abstract  
The adequacy and efficacy of simple and hybrid machine learning and Computational Intelligence algorithms were evaluated for the classification of potential prostate cancer patients in two distinct categories, the high- and the low-risk group for PCa. The evaluation is based on randomly generated surrogate data for the biomarker PSA, considering that reported epidemiological data indicated that PSA values follow a lognormal distribution. In addition, four more biomarkers were considered, namely, PSAD (PSA density), PSAV (PSA velocity), PSA ratio, and Digital Rectal Exam evaluation (DRE), as well as patient age. Seven simple classification algorithms, namely, Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Logistic Regression, Naive Bayes, and Artificial Neural Networks, were evaluated in terms of classification accuracy. In addition, three hybrid algorithms were developed and introduced in the present work, where Genetic Algorithms were utilized as a metaheuristic searching technique in order to optimize the training set, in terms of minimizing its size, to give optimal classification accuracy for the simple algorithms including K-Nearest Neighbors, a K-means clustering algorithm, and a genetic clustering algorithm. Results indicated that prostate cancer cases can be classified with high accuracy, even by the use of small training sets, with sizes that could be even smaller than 30% of the dataset. Numerous computer experiments indicated that the proposed training set minimization does not cause overfitting of the hybrid algorithms. Finally, an easy-to-use Graphical User Interface (GUI) was implemented, incorporating all the evaluated algorithms and the decision-making procedure. prostate cancer (PCa); machine learning classification; Computational Intelligence algorithms; PSA; PSAD; PSAV; PSA ratio; DRE; genetic algorithms; decision trees; random forests; support vector machines; K-nearest neighbors; logistic regression; naive Bayes; artificial neural networks; k-means clustering; GUI +
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The etiology of PCa is multi-factor, including age, considering that is the most common malignancy in elderly men [4], 1 in 350 men under 50 will develop the disease [7], while 1 in 52 men aged 50-59 years and, finally, 6 to 10 over 65 years will develop PCa [8]. Dietary habits that are associated with ethnic differences play an essential role in the development of PCa, like preference for a diet rich in red meat and saturated fats [9; 10; 11; 12; 13; 14; 15; 16; 17], which, as a result, alters the levels of androgens [18; 19], as well as dietary supplementation, with some supplements linked to a high risk of developing PCa, like omega-6 fatty acid, whereas others, like omega-3 fatty vitamin D and E, lower the risk of PCa [20; 21; 22]. Physical activity has a protective effect against many forms of cancer including PCa, a high body mass index (BMI) is strongly associated with more aggressive PCa and poor therapeutic outcomes [23; 24]. This fact is probably due to elevated levels of insulin, which in turn promote the growth and proliferation of PCa cells [25]. Furthermore, obesity often conceals and delays the diagnosis of PCa since it is known that obesity promotes elevated circulating plasma volume and hence the hemodilution of PSA levels [26], making it difficult to recognize the disease in its early stages [25; 27]. Additionally, a PCa factor is the chronic inflammation of the prostate gland. Even though the original prevailing view was that there was not a strong association between prostatitis (inflammation of the prostate) and PCa, studies have shown that the inflammatory process affects the tumor microenvironment and the subsequent angiogenesis and remodeling of the extracellular matrix (ECM) [28]. Among other factors, smoking is responsible for many forms of cancer and PCa [29], as well as sexually transmitted diseases (STDs) [30], family history, and genetic factors [21; 31].