## 2 Literature Review  
Machine learning [5] is a constantly evolving branch of artificial intelligence related to those algorithms that try to simulate human intelligence using information from their environment. Techniques based on machine learning have been used in different fields, such as finance [6], pattern recognition [7,8], and medical applications [9,10]. In machine learning, it is important to carefully choose which features should be used and which should be discarded to construct the models. Medical datasets commonly present a small number of cases with a large number of variables, thus introducing different problems such as dimensionality and high computational requirements [11]. To deal with these obstacles, the use of feature-selection techniques has been proposed to select the variables that provide the greatest value. According to [12], three common FS categories are: filters, wrappers, and embedded methods. Filter methods stand out mainly for their speed and scalability, being of great help in extremely large datasets. Through a series of statistical processes, scores are assigned to the different variables; the ones with the highest scores will be used to create the model. The great limitation of these methods is that they do not consider the relationship between variables. Two examples of this category are [PERSON]'s correlation coefficient, which allows us to quantify the linear dependence between two variables, and mutual information, which seeks to reduce the uncertainty of a random variable through knowledge of another random variable [12; 13]. Wrapper methods search for the most appropriate subset of variables using the selected predictor as a black box to score the different subsets of variables generated throughout the different iterations. Wrapper methods [13] have a high computational cost since they require training and testing for each possible subset. Sequential Feature Selection (SFS) [14] starts with an empty set and adds the different variables individually, looking for the one that contributes the most value to the set. Once identified, this variable will be permanently incorporated into the subset, and then the next iteration will follow, repeating the same process until the desired number of variables is obtained. Based on this implementation, it is possible to find different variants, such as Sequential Backward Selection (SBS), which starts with the complete set of variables and reduces it through iterations, or Sequential Floating Forward Selection (SFFS) [15], which is based on the SFS method and incorporates a backward component with the SBS. An example of the use of this type of technique is [16], where the authors apply a wrapper method, called Recursive Feature Elimination with Cross-Validation (RFECV), to select the best variables for a classification problem in the medical domain and obtain an accuracy improvement. In our work, RFECV is one of the techniques against which we compare the performance of our proposal, since it is a good FS alternative and has the benefits of wrapper methods. Heuristic search methods applied to feature selection can also be considered part of the wrapper methods. Examples of the use of genetic algorithms focused on feature selection can be found in the medical field, these methods being interesting for large datasets. In [17], an optimization algorithm based on a genetic algorithm is proposed, which allows to optimize the values of the SVM parameters, obtaining an optimal subset of features, and improving the classification accuracy. Embedded methods aim to reduce the computational time required to reclassify subsets of distinct variables. In order to do that, it tries to combine the advantages of the filter and wrapper methods. One of the main characteristics of embedded methods is the introduction of feature selection as part of the training process rather than as a separate phase, i.e., the feature-selection process becomes an integral part of the model.