## 3 Methods
### Classification Methods
#### 3.5.3 Classification Model Performance  
The data were initially separated into 80% for training and 20% for testing [39]. To classify the performance of neural and non-neural models, we employed the adaptable 10-fold cross-validation method [71]. Utilizing the resampling technique, this procedure ensures that the input data are not precisely the same, allowing our models to be flexible and responsive to different datasets. The neural and non-neural models were trained with ten partition schemes [72], with the input data for training to change in each of the ten repetitions, demonstrating the adaptability of our approach.  
After training, four statistics served as the basis for evaluating the neural and non-neural methods:  
1. True positive (TP) is the situation in which the method classified an accident with a serious injury as an event that resulted in a severe injury.
2. True negative (TN) is the situation in which the method classified an accident without serious injury as an event that did not result in serious injury.
3. A false positive (FP) occurs when the method classifies an accident without serious injury as an accident with serious injury.
4. A false negative (FN) occurs when the method classifies an accident with serious injury as an accident without serious injury.  
From the number of TP, TN, FP, and FN, the values of accuracy (Equation (1)), precision (Equation (2)), recall (Equation (3)), and F-score (Equation (4)) were estimated:  
\[\text{Accuracy}\,=\,\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FN}+\text{FP} +\text{TN}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Iteration** & **Hyperparameter** & **Values** \\ \hline
1 & Hidden layers & 1, 2, 3, and 4 \\  & Number of input neurons & 9, 19, 29, 39, 49, and 49 \\  & Number of hidden layer neurons & 3, 13, 23, 33, 43, and 53 \\  & Activation function & ReLU, tanh, and Sigmoid \\  & Sequence size & 30, 60, and 90 \\
2 & Dropout & 0.10, 0.20, 0.30, and 0.40 \\
3 & Epoch & 1000 and 2000 \\  & Optimizer & RMSprop, AdaGrad, Adam, and SGD \\
4 & Learning rate & 0.01, 0.001, and 0.005 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of hyperparameters and values tested in LSTM, Bi-LSTM, and GRU.  
\begin{table}
\begin{tabular}{c c c} \hline
**Iteration** & **Hyperparameter** & **Values** \\ \hline
1 & Hidden layers & 1, 2, 3, and 4 \\  & Number of filters & 9, 19, 29, 39, 49, and 49 \\  & Kernel size & 2, 3, 4, and 5 \\  & MaxPooling & 2, 3, and 4 \\
2 & Dropout & 0.10, 0.20, 0.30, and 0.40 \\
3 & Epoch & 1000 and 2000 \\  & Optimizer & RMSprop, AdaGrad, Adam, and SGD \\
4 & Learning rate & 0.01, 0.001, and 0.005 \\ \hline \end{tabular}
\end{table}
Table 3: Summary of hyperparameters and values tested in 1D-CNN.  
\[\text{Precision }=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
\[\text{Recall }=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{F}-\text{score }=2*\frac{\text{Precision }*\text{ Recall}}{\text{Precision }+\text{ Recall}} \tag{4}\]  
Thus, accuracy sought to determine the percentage of correct classifications of the method compared to the total classifications made [47]. Precision or sensitivity is a valuable metric for reducing the number of false positive classifications made by the method [27]. On the other hand, recall aims to estimate the effect of false negative classifications made by the method [73]. Finally, the F-score is a performance measure that unifies the trade-off relationship between accuracy and recall [39].