## 4 Discussion
### Analysis of Other Major Variables Influencing Severity  
#### 4.3.1 Grouping with LCCA  
LCCA can be used to identify data trends via detailed analysis of the major elements included in the group; it can also select major variables by identifying variables that heavily contribute to grouping, similar to the first data preprocessing stage. Advantageously, this method can capture the flow when two or more variables that are difficult to represent in the alluvial flow diagram are connected. The five attributes that are most influential in the differentiability of clusters are presented in Table 6, which summarizes the ratio between the total number of observations in the dataset and the specified cluster. Each cluster can be grouped by clustering objects with high similarity according to the similarity of seven variables out of 963 objects. In the previous study, there is a limitation in selecting the variable by applying LCCA only to binary variables with two elements. However, in this study, LCCA was applied to categorical variables with many elements [1]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cluster ID**} & \multicolumn{2}{c}{**Attributes**} & \multicolumn{2}{c}{**Probabilities**} & \multicolumn{2}{c}{**Being**} & \multicolumn{2}{c}{**Observed**} & \multirow{2}{*}{**Percentage**} \\ \cline{2-3} \cline{5-8}  & **Variable** & **Element** & **of Current** & **Cluster** & **(Total)** & **(in the Cluster)** & \\ \hline \multirow{5}{*}{Cluster 1} & Assailing material & Permanent fixture & \multirow{2}{*}{0.594} & \multirow{2}{*}{244} & \multirow{2}{*}{220} & \multirow{2}{*}{90.16\%} \\ \cline{2-3} \cline{5-8}  & Type of accident & & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & Type of accident & & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & Type of accident & & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & & Outside of the lower body & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & & Body & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & Severity & & & & & \\ \cline{1-1} \cline{2-3} \cline{5-8}  & & Injury & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Five most influential variables and elements in the formation of each cluster. Figure 7: Alluvial flow diagram using variables with large correlation in prediction of severity. A: Year (A1: 2015; A2: 2016; A3: 2017; A4: 2018; A5: 2019; A6: 2020); C: Type of accident (C1: Jamming, C2: Fall down; C3: Fall off; C4: Hit; C5: Collapse; C6: struck; C7: Imbalance/Uncontrolled motion; C8: Occupational diseases; C9: Mutilation/Cut/Puncture; C10: Fire/explosion/blast); D: Injured part (D1: Inside the upper body; D2: Outside of the upper body; D3: Face; D4: Inside of the lower body; D5: Outside of the lower body); S: Severity (S1: Slight injury; S2: Serious injury; S3: Fatal injury). Cluster 1 includes data elements such as \"permanent fixture\" (under assailing material), \"fall down\" and \"fall off\" (under type of accident), outside of the lower body (under injured parts), and \"serious injury.\" Cluster 2 includes data on \"hit\" and \"struck\" accidents (under type of accident), \"third-party liability\" (under cause of accident), \"heavy non-fixture\" (under assailing material), and outside of the lower body (under injured parts). The latter is the case where the number of accident is \"high\" and \"high\", \"high\"  
upper body. Occupational accident data in the construction industry can be distinguished through the selection of differentiated elements between clusters. However, influential elements in each cluster can be partially duplicated in other clusters. For example, Clusters 1 and 4 contain falls as a major attribute among the types of accident; however, they are otherwise differentiated in terms of injured part and severity. By their nature, construction accidents can be affected by numerous variables, and identifying trends may be difficult; however, accident data can be grouped by the relationships between other influential attributes.