## 6 Conclusions  
For a real and correct comparison, two essential and fundamental elements must be fulfilled, namely, (1) comparing the results using two different methods which have the same final goal and (2) using the same test and training signals. If these two essential elements are not present, the comparison is not correct. In our case, the final goal was feature selection and the databases used were the two available on the internet and MATLAB. Unfortunately, we did not find any works that use these two databases for feature selection. And comparing the classification results using a different number of features for these two bases is not correct. Therefore, in the following, we will make some comments to elucidate the difference between the methods we compared and we will point out the advantages and disadvantages of each group of methods. These two major approaches have been analyzed in this study and the conclusions are presented below. Filter methods are a suitable choice when a fast and simple process is needed since they analyze each feature independently of the classification model. Their main advantage is speed, as they do not require model training, making them ideal for large datasets. Additionally, being model-independent, they can be used as an initial dimensionality reduction step. However, an important disadvantage is that they do not consider interactions between features, potentially overlooking important feature combinations. Moreover, the final model's performance may be worse compared to that obtained used more advanced methods. Thus, filter methods are useful for quickly removing irrelevant features without consuming a large amount of computational resources. On the other hand, embedded methods offer a balance between performance and efficiency because they perform feature selection directly during the model training. Unlike filter methods, they consider interactions between features and optimize both the model and feature selection simultaneously. This makes them more precise but also slower, as they require model training. Another disadvantage is that their performance depends on the chosen algorithm, such as the LASSO regression or random forests algorithms. Additionally, they are not as exhaustive as wrapper methods, which evaluate multiple feature combinations. However, if sufficient computational resources are available, embedded methods provide a strong balance between speed and performance. For the oncological database, all embedded methods that were tested achieved maximum classification rates, whereas the random projections method produced results similar to the original dataset. The analysis of the two databases and feature selection by embedded methods vs. filter methods shows that appropriate feature selection plays a crucial role in machine learning-based classification. Filter methods, such as the Kruskal-Wallis and MRMR methods, can maintain or even improve the classification rate by eliminating redundant features, while more advanced methods, such as the BP_ADMM and LASSO methods, demonstrate that feature optimization can lead to superior performance. Therefore, choosing the right method depends on the analysis objective and the trade-offs between complexity, accuracy, and computational efficiency. Conceptualization, M.F., H.-N.C. and L.G. ; methodology, H.-N.C. and L.G. ; software, M.F. ; validation, M.F., H.-N.C. and L.G. ; formal analysis, M.F. ; investigation, M.F. ; resources, M.F. ; data curation, M.F. ; writing--original draft preparation, M.F., H.-N.C. and L.G. ; writing--review and editing, M.F., H.-N.C. and L.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.