## 4 Results
### Result of Model Comparison  
The configuration of the algorithms used in non-neural methods was previously described in the methodology. On the other hand, the process of choosing the best neural network architectures occurred via the hyperparameter tuning process. The best architecture found for the multilayer perceptron (MLP) involved 29 neurons in the input layer, one deep layer with 13 neurons, ReLU activation function, dropout of 0.10, 1000 epochs, 'Adam' optimizer, and a learning rate of 0.01. Regarding LSTM, Bi-LSTM, and GRU, the best architecture involved 29 neurons in the input layer, two deep layers with 13 neurons each, 'ReLU' activation function, 0.10 dropout, 2000 epochs, 'Adam' optimizer, and a learning rate of 0.01. Finally, the best architecture for 1D-CNN involved 29 filters, kernel size equal to 3, a deep layer with 13 filters, MaxPooling equal to 2, dropout of 0.10, 2000 epochs, 'Adam' optimizer, and a learning rate of 0.01. Then, the 10-fold cross-validation method was used to analyze the performance of the neural and non-neural models (Table 6). When analyzing the average parameters, it was noticed that, except for LSTM and GRU, all methods presented an accuracy greater than 90%. Among the non-neural methods, RF, XGBoost, and DT presented accuracy equal to 95.9%, 95.7%, and 94.4%, respectively. Therefore, tree-based methods performed slightly better than LR (93.7%) and SVM (94.0%). On the other hand, neural methods showed accuracy greater than 95%, such as 1D-COV (95.8%), Bi-LSTM (98.6%), and MLP (99.6%). Thus, when comparing the performance of the three best neural and non-neural methods, it was observed that the neural methods showed slightly higher accuracy than the non-neural ones, with a maximum accuracy gain of 3.7%. However, it is worth noting that both neural and non-neural methods showed a high rate of correct classifications of serious accidents (TP) and non-serious accidents (TN), resulting in high accuracy values. Regarding accuracy, the non-neural RF and XGBoost methods stood out, reaching 94.7% and 94.6%. Therefore, XGBoost proved slightly more accurate when classifying serious accidents (TP). On the other hand, neural methods such as 1D-COV (99.8%) and LSTM (99.4%) showed accuracy greater than 99%. Therefore, this result suggests that the LSTM method was penalized in accuracy due to its difficulty in correctly classifying non-serious accidents (TN). The 1D-COV method shows great potential due to its high accuracy and precision. When comparing the top results from neural and non-neural models, it was observed that a modest increase in accuracy can reach up to 5.1%. Regarding recall, the non-neural RF (95.2%) and XGBoots (94.4%) methods presented the best results. However, the recall of the 1D-COV (99.7%) and Bi-LSTM (99.2%) methods was greater than 99%. A discreet maximum recall gain of 5.3% can be observed when comparing the best models. In general, it is suggested that these methods can correctly classify non-serious accidents (TN). Methods such as LSTM, for example, presented recall equal to 82.7%, that is, some difficulty in classifying non-serious accidents, even though their precision (ability to classify serious accidents) was high. The F1-score, due to its relationship with precision and recall, only reinforced the better performance of the non-neural methods RG (94.8%) and XGBoots (94.4%), and the neural methods Bi-LSTM (99.0%) and 1D-COV (99.7%). When considering all parameters, regardless of the type of method, a ranking can be formed, such that the order must be 1D-COV, Bi-LSTM, MLP, RF, XG, SVM, LR, DT, GRU, and LSTM.