## 3 Methods
### Classification Methods
#### 3.5.1 Non-Neural Methods  
The non-neural methods were developed with the help of the scikit-learn [57] and xgboost [58] libraries. All methods are classic and require some coding of variables. The first model tested was logistic regression (LR). A generalized linear model seeks to relate qualitative dependent variables to multiple independent variables. Authors such as [PERSON] et al. [59] suggest that other ML methods do not present significant benefits when compared to predictions made via LR for predicting clinical factors. Thus, LR presented itself as an appropriate method for this study. The algorithm was implemented with penalty type '12', tolerance for stopping criterion equal to 0.0001, the maximum number of interactions for convergence equal to 100, and solver type 'lbfgs' (limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm).  
The decision tree (DT) method was also tested. This method seeks to predict a target variable based on the values of other partitioned input variables, giving rise to hierarchical logical diagrams [27]. It is a non-parametric data method with non-linear relationships. This ML algorithm follows a logic of nodes, with the first node being called the decision node, which has several branches connected to leaf nodes, being the output of the decision node [60]. Recently, DT has been successfully used in classification problems in the footwear industry [53]. Therefore, it was one of the non-neural methods tested. The DT algorithm was implemented with the Gini criterion to measure the quality of the divisions in the tree; the division of each node used the best criterion instead of the random criterion, and the minimum number of samples to divide an entire node was equal to 2. The minimum number of samples for each leaf node was equal to 1, and no number of resources was defined to be considered when looking for the best division of the tree.  
The random forest (RF) method uses the same principle as DT to generate several trees from vectors with random values, and its main advantage is minimizing overfitting [40]. Authors such as [PERSON] et al. [61] highlight that RF makes use of the bagging algorithm to generate training data via a sampling process, in which the number of samples generated for training will be equal to the number of samples in the source dataset, making use of the index of Gini to estimate the number of classes in the input data and the heterogeneity of the parent nodes and child nodes of the trees. RF is the most widely used ML algorithm [62]. Therefore, RF was one of the methods used in this article. For the RF algorithm, among the available options, the Gini criterion was used to measure the quality of the divisions in the trees, the square root (sqrt) was used to define the number of resources to be considered when searching for the best division, and the bootstrap as a resampling technique to select training data.  
Developed from the improvement of RF-based algorithms (specifically the gradient boosting decision tree), the extreme gradient boosting (XGBoost) algorithm seeks parallel speed and has strong fault tolerance and excellent generalization power [63]. In summary, XGBoost verifies the need to add a new tree to the set of trees initially generated to reduce the residual error (calculated from the results found between the actual value and the value coming from the previously computed set of trees) [61]. As XGBoost has been suggested in the literature as a method capable of classifying injuries caused by accidents [34], we alsodecided to test this method in this article. The default present in the xgboost library [58] was adopted in the development of this algorithm.