## 3 Results and Analysis
### Second Data Preprocessing for Reduction of Elements
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variables** & \begin{tabular}{c} **Predictor** \\ **Importance** \\ \end{tabular} & **Rank** & \begin{tabular}{c} **Chi-Square** \\ **\(p\)-Value** \\ \end{tabular} & **Rank** & **Cramer’s V** & **Rank** & \begin{tabular}{c} **Cluster** \\ **Group** \\ \end{tabular} & \begin{tabular}{c} **Selected** \\ **Variable** \\ \end{tabular} \\ \hline \begin{tabular}{c} Accident \\ classification \\ \end{tabular} & 0 & 12 & \(7.09\times 10^{-6}\) & 6 & 0.12 & 10 & X & - \\ \hline Headquarter & 0 & 12 & \(5.74\times 10^{-1}\) & 14 & 0.04 & 14 & O & - \\ \hline Process rate & \(1.36\times 10^{-4}\) & 6 & \(4.46\times 10^{-1}\) & 12 & 0.10 & 12 & X & - \\ \hline Year & \(4.46\times 10^{-4}\) & 5 & \(1.20\times 10^{-8}\) & 5 & 0.17 & 5 & O & \(\surd\) \\ \hline Month & \(1.06\times 10^{-4}\) & 10 & \(1.84\times 10^{-2}\) & 10 & 0.14 & 8 & X & - \\ \hline \
\begin{tabular}{c} Day of the \\ week \\ \end{tabular} & 0 & 12 & \(9.03\times 10^{-1}\) & 15 & 0.06 & 13 & X & - \\ \hline Hour & \(1.29\times 10^{-4}\) & 7 & \(3.42\times 10^{-2}\) & 11 & 0.17 & 6 & X & - \\ \hline Age & \(4.21\times 10^{-5}\) & 11 & \(1.17\times 10^{-2}\) & 9 & 0.11 & 11 & X & - \\ \hline Gender & \(1.11\times 10^{-4}\) & 9 & \(1.73\times 10^{-4}\) & 7 & 0.13 & 9 & X & - \\ \hline Type of work & \(6.65\times 10^{-4}\) & 4 & \(8.55\times 10^{-3}\) & 8 & 0.16 & 7 & O & \(\surd\) \\ \hline Type of accident & \(2.07\times 10^{-3}\) & 2 & \(1.59\times 10^{-20}\) & 2 & 0.27 & 2 & O & \(\surd\) \\ \hline Injured part & \(7.94\times 10^{-3}\) & 1 & \(2.36\times 10^{-78}\) & 1 & 0.48 & 1 & O & \(\surd\) \\ \hline Work place & 0 & 12 & \(5.30\times 10^{-1}\) & 13 & 0.04 & 15 & O & - \\ \hline
\begin{tabular}{c} Assailing \\ materials \\ \end{tabular} & \(1.15\times 10^{-3}\) & 3 & \(1.26\times 10^{-9}\) & 4 & 0.25 & 3 & O & \(\surd\) \\ \hline Cause of accident & \(1.14\times 10^{-4}\) & 8 & \(9.76\times 10^{-11}\) & 3 & 0.19 & 4 & O & \(\surd\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of four methods applied to severity for extraction of major variables. In the ensemble method, the errors before and after nested CV were small, and relatively minimal overfitting was predicted to occur compared to the SVM model. In the nested CV prediction results for eight cases, the ensemble method was predicted to achieve a ~10% higher accuracy than the SVM method, and it was found to be more suitable for datasets containing a variety of variables and elements. When predicting severity by reducing the elements of a variable, we found that if all three variables were reduced, the ensemble method showed an accuracy of 67.29%, only ~3% lower than the case without reduction. This indicates that the characteristics of the data did not change significantly. Therefore, reducing the elements in the data made it easier to analyze the accident data and identify trends, and it simplified the analysis of complex data in which correlations are difficult to find. A final dataset--featuring seven variables and a maximum of ten elements--was formed by selecting the major variable through the first data preprocessing stage and standardizing the elements through the second one. Similar results were obtained when selecting each of the seven variables as outputs and predicting using ML; thus, we concluded that this final dataset was valid.