## 4 Discussion
### Correlation Analysis between Elements  
In order to apply the accident analysis results to the safety measures at the construction site, it is necessary to pay attention to the correlation between variables contributing to the accident, rather than simply increasing the prediction accuracy [37; 48]. Correlations between variables can be analyzed through contribution and network analyses; however, these analyses struggle to capture the correlations between the elements included in the variables. Therefore, in Figure 5, a detailed correlation analysis is shown for the top three variables in terms of F1 score (severity, injured area, and type of accident, respectively) using an alluvial flow diagram; these three variables strongly contributed to predicting the type of accident. The trends of correlation contribution to the type of accident show that injuries on the outside of the upper body occurred mostly as a result of \"fall down\" due to \"heavy non-fixture\" or \"light non-fixture (equipment),\" or due to the \"carelessness\" of the victim. Here, it is thought that such accidents can be prevented if workers who work \"heavy non-fixture\" or \"light non-fixture (equipment)\" are aware of accident cases through pre-work education. Figure 6 shows an alluvial flow diagram for the type of accident and severity, which both contribute strongly to the prediction of the injured part. Overall, serious injuries were found to occur most often due to \"fall off\" and \"fall down\" accidents; it can also be seen that injuries occurred to the outside of the upper or lower body. The most fatal injuries are seen to occur as a result of falling accidents, and injuries to the face or upper body were most common. By analyzing these overall trends, we expect to be able to reduce the occurrence of accidents by providing customized safety training and safety protection equipment to workers in high-risk roles. Figure 4: Visualization of contribution network between occupational accident variables. The variables that primarily contribute to severity prediction are year, type of accident, and injured part; the alluvial flow diagram for these factors is shown in Figure 7. In the relationship between year and type of accident, \"fall down\" can be seen to be the most prevalent accident across most years, followed by \"fall off.\" The most frequently injured areas were the outside of the upper and lower body, and most of these were found to suffer from serious or slight injuries; fatal injuries were most frequently caused by lower-body injuries due to falling accidents. Moreover, a strong correlation was confirmed between fatal injuries and accidents in which the victim's head was hit by an object. The correlations were analyzed for variables with large correlations when the output was found to differ in the alluvial flow diagram. Through the two data preprocessing steps, the complexity of the initial construction accident data was resolved, and the correlations of construction accidents could be readily understood through the alluvial flow diagram using variables with a large correlation to the output. Through this, we expect to be able to help prevent construction accidents, provided appropriate safety measures are established for the specific accident types. However, in alluvial flow diagram analysis, identifying detailed trends can be difficult because the flow of the previous variable is  
Figure 5: Alluvial flow diagram using variables with high correlation when predicting type of accident.