## 4 Discussion
### Analysis of Other Major Variables Influencing Severity
#### 4.3.2 Visualization with PCA  
In general, the PCA method selects a major variable that can be easily used to classify data, by finding a variable with a large influence across many variables and utilizing it to reduce the dimensions of the variable. PCA primarily uses numerical data [51]; categorical data are difficult to use because they do not have separate numerical values according to the variables and items. In a study that applied PCA based on the construction industry survey data, scores were set for each item and used to conduct PCA and reduce dimensions using numerical values [38]. In this study, four methods were used to identify major variables; then, PCA was applied as a visualization method rather than a dimension-reduction one. To visualize the severity level, which was the variable with the highest predictive accuracy as determined through ML analysis, the major variables (e.g., year, type of accident, and injured part) were used as PCA data. The character-type categorical data were converted into numeric-type categorical data and utilized. Figure 8 shows the results of PCA using three variables, and each data point is displayed in red, blue, and light green, depending on the severity \begin{table}
\begin{tabular}{c c c} \hline \hline
**Cluster ID.** & **Conceptual Definition** & **Number of Data Entries (Percentage (\%))** \\ \hline \multirow{3}{*}{Cluster 1} & Serious injury to the outside of the lower & \multirow{3}{*}{Cluster 1} \\  & body from a permanent fixture (floor, etc.) & \multirow{3}{*}{369(38)} \\  & by fall down and fall off accidents & \\ \hline \multirow{3}{*}{Cluster 2} & Injury to the outside of the upper body from & \multirow{3}{*}{259(27)} \\  & being hit or struck by a heavy non-fixture & \\  & (construction material) & \\ \hline \multirow{3}{*}{Cluster 3} & Injury due to mutilation, cut, or puncture & \multirow{3}{*}{165(17)} \\  & on the outside of the upper body whilst & \\  & using light non-fixture (equipment) & \\ \hline \multirow{3}{*}{Cluster 4} & Fatal injury on the inside of the upper body & \multirow{3}{*}{118(12)} \\  & and face from fall off & \\ \hline \multirow{3}{*}{Cluster 5} & Injury to the inside of the lower body & \multirow{3}{*}{53(6)} \\  & (pelvis) from unbalanced and uncontrolled & \\ \cline{1-1}  & movements during heavy non-fixture work & \\ \hline \multirow{2}{*}{Total} & \multirow{2}{*}{963(100)} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Concept definitions of construction accidents by cluster. level. By plotting the major variables through PCA, the severity levels can be distinguished relatively clearly. Moreover, it can be seen that the severity level is classified by the injured part (PC3) rather than the year (PC1) or type of accident (PC2); this shows that the variable most strongly correlated with severity in the correlation analysis is the injured part. In other words, it is possible to predict the severity through PCA classification using three major variables. In this study, in order to overcome the limitations of the current accident data processing, the correlation between major variables derived from occupational accident data was constructed by various ML algorithms and new data process procedure. In previous studies, only one method was used to select major variables, whereas, in this study, four methods were used to select the main variables. Next, there is a difference in selecting an optimized element at a point where prediction accuracy is maintained using the ML method. In addition, for accident analysis currently used in the field, risk assessment is the most representative method, mainly to derive hazard factors based on experience, to calculate severity by intensity and frequency, and to prepare countermeasures. However, this method of this study is a case analysis using the result of accident prediction analysis for each variable of accident data, which is more suitable for actual accidents.