A Support Vector Machine (SVM) algorithm works by dividing training data into two categories, either by linear or nonlinear classification; new data are then assigned to one of the two classes. The model separates the data by finding a hyperplane that maximizes the gap between categories. In the case of linear classification, the space is two-dimensional, making the hyperplane a linear function. When the data are not linearly separable, the algorithm performs the kernel trick, which involves increasing the dimensions of the data space. This results in the hyperplane being able to be another function in the original space, such as radial or polynomial, allowing to classify the data in different ways. We split our datasets in the same manner as the other classification models and set Models A and B as support vector machines given by the 'e1071' package in R. We decided to compare the performance of using a linear kernel (SVML) and a polynomial kernel (SVMP). Radial kernels were not evaluated because our binary data do not follow a circular separation in the hyperplane; therefore, our classifications do not provide an adequate fit. The hyperparameter selection for each model was performed with the trainControl and train functions from the 'caret' package, which executes a k-fold cross-validation (k = 10 was used), where different values of the parameters were tested and selected, which resulted in the highest accuracy. The best hyperparameters were the function's default parameters: C = 1 for SVML and for SVMP, C = 1, degree = 3, gamma =1, and coef0 = 0. We calculated the accuracy, sensitivity, and specificity of each model using Eq. 9-11, using the results from their respective confusion matrices (see Tables ). We then compared the confusion matrices of the LR, RFC, SVML, and SVMP models to determine the one that yielded the best results.