## 2 Related Works  
Currently, in the field of ML, predictions about various types of diseases are performed using different methods. One of the most widely used methods is based on decision trees. In [24], the performance in terms of precision, sensitivity, specificity, and accuracy of the tests was compared to the free Heart Disease dataset of the University of California, which is composed of 13 features. In this study, the data mining algorithms C5.0, II.2., support vector machine (SVM), k-nearest neighbors (KNN), and neural network (NN) were used to build a model to predict heart disease. C5.0 built the model with the highest accuracy, at 93.02%; KNN, SVM, and NN achieved accuracies of 88.37%, 86.05%, and 80.23%, respectively. [PERSON] et al. [25] applied an integration of the results of the machine learning analysis to different data sets aimed at Coronary Artery Disease (CAD). This avoids missing, incorrect, and inconsistent data problems that may appear in data collection. The fast decision tree and the pruned C4.5 tree were applied and the resulting trees were extracted from different data sets and compared. The common characteristics among these data sets were extracted and used in the subsequent analysis of the same disease in any data set. The results showed that the classification accuracy of the collected data set was 78.06%, which was higher than the average classification accuracy of all separate data sets, which was 75.48%. [PERSON] et al. [26] proposed a model to predict coronary artery disease, applying multifactor dimensionality reduction (MDR) and recursive partition (RP). The ensemble machine learning algorithm (EMLA) was tested on a set of 648 records consisting of 364 cases of CAD and 284 healthy records, showing outstanding performance compared to other models for cataloging the disease with 89.3%, and a stenosis prediction accuracy of 82.5%. [PERSON] et al. [27] compared prediction and classification techniques such as naive Bayes, decision trees J48, and the bagging algorithm through the evaluation of the precision with which the model was constructed. Subsequently, a cross-validationof 10 iteration data must be performed to estimate the unbiased accuracy of the model. The model with the highest classification accuracy was bagging, with 85%, compared to naive Bayes with 83% and J48 with 84%. Some of the most widely used methods in this area are those based on SVM. In [28], liver diseases were predicted using classification algorithms. The algorithms used in this work were naive Bayes and SVM. These classification algorithms were compared according to performance factors, that is, the accuracy of the classification and the execution time. From the experimental results, SVM was found to be highly accuracy with 76.6%, compared to the 59% of naive Bayes. SVM was found to be superior for predicting liver diseases. [PERSON] et al. [29] used SVM to construct the classifier and compared it with logistic regression (LR) using demographic, clinical, and magnetic resonance tomography (MRI) data obtained in years one and two to predict multiple sclerosis (MS) disease at five years of follow-up. By adhering to clinical and brain data in the short term, using corrective measures of class imbalance, and considering classification costs to the SVM, SVM showed promise in predicting the course of MS disease and in the selection of suitable patients for more aggressive treatment regimens. The use of non-uniform misclassification costs in the SVM model increased sensitivity, with prediction accuracies of up to 86%. Related to models based on neural networks, we can highlight the work of [30]. They proposed a hybrid method between artificial neural network (ANN) and fuzzy analytical hierarchy process (AHP) for the prediction of the risk of heart failure, obtaining better results in prediction tests, cross entropy, and receiver operating characteristic (ROC) graphs compared to the conventional ANN. [PERSON] et al.