## 4 Conclusions
All these three hybrid algorithms were able to detect training sets with sizes that were 16.06% of the total cases for the GAKNN algorithm, 16.37% for the GACLUST algorithm, and 33.64% for the GAKMEANS algorithm, which achieved optimal classification accuracy on the test set equal to 100%. Indicative of the improvement in the results of the hybrid algorithms is that the simple KNN algorithm, which, according to Figure 4 and Table 4 also showed accuracy below 90% even for training sets with a size of 70% of the total cases. With the use of GA optimization, it achieved a performance of 100% in classification accuracy on the test set, using only 16.06% of the cases in the training set. Based on the preliminary though very encouraging results presented in this paper, in future work, we intend to investigate issues that were raised but not studied in the required depth in the context of this paper. These issues include (a) the performance of the remaining simple classifiers when integrated into corresponding hybrid algorithms, in the same way that the behavior of the simple KNN algorithm and the hybrid GAKNN algorithm were investigated, also considering the fine tuning of the hyper-parameters of these classifiers, for example, the number of neighbors considered in the KNN algorithms, the number of hidden neurons, the learning rate, the activation function, etc., in the ANN algorithm, and so on, (b) the performance of hybrid algorithms for more complex fitness functions including additional performance metrics such as sensitivity and specificity, (c) the role of the _alpha_ parameter in the behavior of the fitness function (Equation (2)), as the conducted experiments indicated that the appropriate value of this parameter can improve or worsen classification performance, (d) the further investigation of the behavior of the GA from the perspective of _emerging behavior_, especially since it appears to involve intelligent characteristics, as it was clearly shown in the case of the GAKMEANS algorithm, and (e) given that the proposed hybrid algorithms are of general purpose, in future work, they will be applied on other datasets that are used for machine learning classification algorithms benchmarking. Last but not least, since a simple and easy-to-use GUI was developed in the context of this work, which can be used to assist in making a medical decision about whether or not to perform a prostate cancer biopsy in order for the possibility of prostate cancer to be evaluated, we intend to further enrich this environment taking into account the findings obtained from the application of hybrid algorithms of this research, as well as everything that arises in the future. Finally, after a long reviewing procedure of the present work, the authors cordially thank all the reviewers for their constructive comments. Conceptualization, D.M. and A.A.; methodology, D.M. and A.A.; software, D.M. and A.A.; validation, D.M. and A.A.; formal analysis, D.M. and A.A.; investigation, D.M. and A.A.; resources, D.M. and A.A.; data curation, D.M. ; writing--original draft preparation, D.M. and A.A.; writing--review and editing, D.M. and A.A.; visualization, D.M. and A.A.; supervision, A.A.; project administration, D.M. and A.A.; funding acquisition, none. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are not available in public, but can be provided on request, by e-mailing to the correspondence author. The authors declare no conflicts of interest.