## 3 Results and Analysis
### Prediction of Various Dependent Variables  
In the second data preprocessing stage, the injured part was expected to strongly correlate with the severity prediction; in some cases, the prediction accuracy was significantly lowered when elements of other variables were simultaneously changed. Therefore, the correlations between variables were analyzed because some variables may be highly influential among the seven variables. In addition, an analysis was conducted to confirm the predictability of other variables, instead of simply predicting the severity of the seven variables. Correlation analysis was performed through ensemble-based prediction and predictor importance calculation because the ensemble method is more suitable than the SVM for the data in this study. Similar to the first data preprocessing step, seven predictions were made with one variable as an output and the other as an input, and the accuracy, precision, recall, and F1 score were calculated as reliability indicators; the results are presented in Table 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Case** & **ML Method** & **Accuracy** &
\begin{tabular}{c} **Accuracy** \\ **(Nested CV Applied)** \\ \end{tabular} \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{A-B(15)-C-D(12)-E(21)-F} & Ensemble & 72.17\% & 70.19\% \\ \cline{2-5}  & & SVM & 94.60\% & 58.32\% \\ \cline{2-5}  & & Ensemble & 71.34\% & 69.26\% \\ \cline{2-5}  & & SVM & 93.98\% & 55.87\% \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{A-B(15)-C-D(5)-E(21)-F} & Ensemble & 68.02\% & 69.57\% \\ \cline{2-5}  & & SVM & 93.67\% & 55.66\% \\ \cline{2-5}  & & Ensemble & 68.33\% & 69.26\% \\ \cline{2-5}  & & SVM & 92.63\% & 54.36\% \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{A-B(5)-C-D(12)-E(21)-F} & Ensemble & 69.57\% & 66.87\% \\ \cline{2-5}  & & SVM & 91.28\% & 57.54\% \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{A-B(5)-C-D(12)-E(6)-F} & Ensemble & 68.54\% & 66.87\% \\ \cline{2-5}  & & SVM & 89.30\% & 56.41\% \\ \hline \multirow{3}{*}{7} & \multirow{3}{*}{A-B(5)-C-D(5)-E(21)-F} & Ensemble & 67.29\% & 66.87\% \\ \cline{2-5}  & & SVM & 89.62\% & 55.02\% \\ \cline{2-5}  & & Ensemble & 67.08\% & 67.29\% \\ \cline{2-5}  & & SVM & 87.54\% & 55.45\% \\ \hline \hline \end{tabular}  
* A: year; B: type of work; C: type of accident; D: injured part; E: assailing material; F: cause of accident.  
\end{table} 
Table 4: Machine Learning (ML) prediction result after the reduction of elements under variables. In general, the proportions of element data in the output are similar; furthermore, when analyzing two elements, the accuracy was used to evaluate the model performance. However, because this study's occupational accident data contained variables with more than five elements, it was difficult to accurately evaluate the model reliability using accuracy alone. Therefore, the F1 score was calculated and analyzed, and the developed model's reliability evaluation results are presented in Table 5. Here, for each element, a true positive (TP) denotes a value that correctly predicts the correct (actual) result, a false positive (FP) denotes a value that incorrectly predicts the correct (actual) result, and a false negative (FN) is a value that incorrectly predicts the wrong (non-actual) result. The precision was calculated using Equation (1) for the TP and FP in each class, and the recall was calculated using Equation (2) for the TP and FN in each class. After that, the average precision and recall were calculated using Equations (3) and (4), and the F1 score was calculated using Equation (5). \[Precision=\ TP/(TP+FP) \tag{1}\]  
\[Recall=\ TP/(TP+FN) \tag{2}\]  
\[Average\ Precision=\{P(A)+P(AD)+P(C)+P(CD)\}/4 \tag{3}\]  
\[Average\ Recall=\{R(A)+R(AD)+R(C)+R(CD)\}/4 \tag{4}\]  
\[\text{F1 Score}=2\times\frac{Average\ Precision\times Average\ Recall}{Average\ Precision+Average\ Recall} \tag{5}\]  
In the SVM and ensemble predictions with the nested CV, the accuracy of most results exceeded 50%; however, in the predictions for year, the accuracy was relatively low.