## 3 Results and Analysis  
### First Data Preprocessing for Selection of Major Variables  
Since it is difficult to perform meaningful analysis by simply predicting construction accident data including various variables and elements with ML methods, the standardization and preprocessing of data are essential. Therefore, the first data preprocessing was carried out to derive the main variables that have a major influence on the construction accident. First, LCCA was conducted using XLSTAT (2020) software, to select key variables in the construction accident data. Datasets containing both binary and categorical variables were used for the analysis because the type of data does not affect LCCA implementation. Furthermore, LCCA was first applied as a data preprocessing method, because it can group without specifying a separate target. First, grouping was conducted by increasing the number of clusters from 1 to 10, to determine the optimal number for classifying accident data. Then, the statistical values of BIC, AIC, and CAIC and the entropy R-squared (indicator) values were checked, to determine the optimum number of clusters; the most suitable way to determine this number is to observe the decrease in BIC, AIC, and CAIC and select the point at which the value remains stable after a certain point. The AIC value decreased as the number of clusters was increased; however, the BIC and CAIC values began to stabilize after the number of clusters reached 5. The entropy R-squared value was used as another control criterion. R values close to 1.0 indicate the importance of the model. However, large multivariate datasets generally tend to become more important when the number of clusters increases, owing to the high level of heterogeneity; thus, they play an ancillary role in determining the optimal model. After dividing the data into 1 to 10 clusters, we found that when the number of clusters was 5, the BIC value was 18,500, the CAIC value was 17,800, and the entropy R-squared value began to stabilize at 0.93. Therefore, the appropriate number of clusters was determined to be 5. Figure 2 shows the results of dividing the initial dataset into five clusters. Eight variables (e.g., accident classification, process rate, and month) included in the five clusters at the same rate did not affect grouping; therefore, they were excluded from the major variables, and nine variables showing differentiation from other cluster groups were selected. When using the variables selected by LCCA in the first data preprocessing step (i.e., headquarter, year, type of work, type of accident, injured part, workplace, assailing materials, cause of accident, and severity), it was found that the construction accident data could be grouped more accurately using five clusters. Although LCCA was able to select nine major variables [1], the reliability of extracted major variables can be increased if a common one is selected using various methods rather than selecting them using only the results of one method. Therefore, the predictor importance and the independence of variables were calculated using the chi-square, Cramer's V, and ML methods, which are generally used to calculate the independence of variables and identify highly correlated variables in text and categorical data analysis. The relationships between variables in the accident data were analyzed. From the 16 variables, one was used as the output, and the remaining 15 were used as inputs, to find the most important variables. Variables that contributed to the predicted output for 16 cases were found to be generally similar, and Table 3 shows the severity results for the predictions. Among the 15 input variables predicting severity, six that were used by all four methods were included among the eight most significant variables. The selected variables were year, type of work, type of accident, injured part, assailing materials, and cause of accident; in total, seven variables (including severity) were determined to exhibit a strong correlation.