## 4 Results and Experimental
### Comparison of Experiment Results  
Based on 2.1 million \"accident summary\" data recorded as actual industrial accident cases in Korea, we proposed three \"occurrence type\" classification models to distinguish the main characteristics of the accidents using the KoBERT model. Each model was tested by sequentially adding data preprocessing, keyword replacement, and morphological analysis to the original data. The results are illustrated in Figures 10-12. Figure 9: Difference between accuracy and loss for the models. (**a**) Classification model performance on the difference between training and validation accuracies. (**b**) Overfitting tendency due to the difference between training and validation losses. Figure 11: Confusion matrix result of Model 2. Figure 10: Confusion matrix result of Model 1. A confusion matrix, validated by class and expressed in terms of precision, recall, loss, and F1 score, was established to compare the results of the overall model. The algorithm and experimental results for each classification model are further discussed. The results of the Model 1 algorithm with only the data preprocessing method are listed in Table 12, and the precision, recall, F1 score, and loss were 87.1%, 0.878, 0.878, and 0.38, respectively. Comparing the results in Table 13 based on the data of each trained class, drowning (67%), asphyviation (0%), and unspecified (0%), which have relatively few data (64-100), exhibited a low classification accuracy or no classification. Conversely, slips and trips, falls, being caught in/between objects, work-related diseases, cuts-lacerations-punctures, and avulsions, which comprised more than 50,000 data points, exhibited a classification accuracy of a minimum of 82% (up to 92%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
\begin{tabular}{c} 84\% \\ 88\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of confusion matrix results for top 5 class and bottom 5 class models. Figure 12: Confusion matrix result of Model 3. Thus, Model 1 exhibited a large variation in classification accuracy, depending on the amount of trained data. The results of the Model 2 algorithm, which involved data preprocessing and data augmentation to improve the accuracy deviation of Model 1, are listed in Table 12, with precision, recall, F1 score, and loss of 91.3%, 0.912, 0.913, and 0.31, respectively. As listed in Table 13, the classification results improved from 67% to 99% for the drowning class, from 0% to 90% for the unspecified class, and from 0% to 99% for asphyxiation, which had low accuracy in the Model 1 algorithm. However, some classification accuracy decreased for other classes that were equalized with data augmentation for classes with insufficient training data. Conversely, increasing the training data to 20,000 for classes with very few data (between 60 and 100) increased the classification accuracy to over 90%, whereas reducing the training data from 50,000+ to 20,000 resulted in an accuracy decrease to approximately 9% or less for classes that were trained with less data. Although an overall improvement in classification accuracy compared to Model 1 was observed, the accuracy decreased in some classes due to the equalization of training data; therefore, Model 2 required further improvement. Model 3 was constructed by adding morphological analysis to the algorithm to address the issues in Model 2. With the improvement, the results of Model 3 were 93.1%, 0.931, 0.031, and 0.24 for precision, recall, F1 score, and loss, respectively, as shown in Table 12. As listed in Table 13, the accuracy of each class, which decreased in Model 2, was improved by up to 6%, and the classification accuracy was over 90%, excluding 6 out of 27 classes (struck by object, fall, crushing or overturning, slips and trips, work-related diseases, and hit). Based on the experimental and analytical results of each variable model, we propose Model 3 as a valid algorithm for the objective classification of accident cases.