## 5 Discussion  
Data are the fundamental resource for any algorithm or model. The results and conclusions can be reached depending on the dataset quality and the target. Therefore, the first issue analyzed in this study was the dataset. [PERSON] et al. [30] state that SL techniques in ML have demonstrated good results when the dataset's most successful characteristics or attributes are chosen. The Pearson correlation coefficient was utilized to identify the correlation between the attributes and general relationships. It is noteworthy that when multiple related attributes pertain to a common area, for example, attributes related to climatic conditions, selecting the most representative attribute is sufficient to avoid the need to select the remaining related attributes. This approach also helps to reduce redundancy. Sometimes, selecting a larger number of related features may not improve the accuracy of algorithms and may even result in no advantage. [PERSON] et al. [30] also note that the dataset must be balanced for an ML algorithm to be effective. This affirmation is especially important in SL, where an imbalance in the predictor class can cause the algorithm to favor predicting the classes with the largest number of samples while performing poorly on classes with fewer samples. This study validated that prediction accuracy is poor when using an unbalanced dataset. This problem is further complicated when dealing with a dynamic dataset that constantly adds new information. However, techniques like undersampling can be used to maintain balance in the number of samples for each predictor class category. This study analyzed a dynamic dataset approach by only updating the values of a few more driving event tuples without changing the total number of samples. The purpose was to observe if there were relevant changes in the results and performance metrics of the models. Nevertheless, there was no evidence to confirm that this process affects the training process; for example, when we added new data and the correct balance of the predictor class was maintained, it was not proven to significantly negatively impact the performance or accuracy of the models. It was not be possible to obtain conclusive evidence since we did not work with a larger amount of data. However, it is evident that when the dataset grows, the training times increase and the prediction accuracy varies. The issue of dynamic datasets can be analyzed in greater depth in future work. Figure 13: Model processing times. On the other hand, focusing on the analyzed DL, RBF, and ML models, we refer firstly to what [PERSON] and [PERSON] mentioned [11] about DL approaches; the most used DL networks are CNNs and RNNs. This statement can be explained by the fact that this approach obtains robust models and develops a good generalization of a particular problem. In this study, we were able to provide evidence, particularly with the CNN-RF, where its prediction accuracy was the best compared to the rest of the analyzed models; the applied cross-validation technique indicated that the DL models, CNN and CNN-RF, obtained the best accuracy results (0.9411 and 0.9604), respectively. Hence, [PERSON] et al. [9] also mention that many researchers in recent times are using DL networks to create models for risk assessment and prediction. Another aspect that is also important to mention about DL is the fact that when using the CNN in combination with a classification algorithm like RF, the prediction accuracy increased; so, for example, with the CNN model, a prediction accuracy of 0.9411 was obtained, while when this same ANN was combined with the RF, this new model obtained a prediction accuracy of 0.9604. From this fact, we can also affirm that a CNN could obtain better results when working with another algorithm, at least if it is a classification algorithm. However, the critical aspect of these DL approaches that we discussed is the time consumed to evaluate and train models.