## 3 Results
### Exploring the Major Features for Predicting Anxiety Due to COVID-19 by Using Adaboost  
Figure 3 shows the results of calculating the feature importance of variables to confirm the key features of anxiety caused by COVID-19 using AdaBoost. The model confirmed that education level, awareness of neighbors/colleagues' COVID-19 response, age, gender, and subjective stress were five key variables with high weight in predicting anxiety induced by COVID-19 for adults living in South Korean communities. The predictive performance of the model to predict anxiety due to COVID-19 using AdaBoost included accuracy = 0.815 (Figure 4), AUC = 0.718, precision = 0.774, recall = 0.815, and F1-score = 0.767.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Variables**} & \multicolumn{2}{c}{**Experienced Anxiety Due to COVID-19**} & \multicolumn{1}{c}{\(p\)***** \\ \cline{2-4}  & **No (n = 170,838)** & **Yes (n = 39,768)** & \\ \hline Presence of hypertension & & & \textless{}0.001 \\ Yes & 44,717 (75.5) & 14,525 (24.5) & \\ No & 126,103 (83.3) & 25,238 (16.7) & \\ Whether knowing own blood glucose level & & & \textless{}0.001 \\ Yes & 51,328 (80.2) & 12,710 (19.8) & \\ No & 118,589 (81.5) & 26,854 (18.5) & \\ Presence of diabetes & & & \textless{}0.001 \\ Yes & 18,588 (74.8) & 6258 (25.2) & \\ No & 152,232 (82.0) & 33,503 (18.0) & \\ Economic activity & & & 0.001 \\ Yes & 105,405 (83.0) & 21,651 (17.0) & \\ No & 65,379 (78.3) & 18,112(21.7) & \\ Occupation & & & \textless{}0.001 \\ Employers and the self-employed & 30,169 (80.5) & 7325 (19.5) & \\ Wage workers & 69,134 (85.2) & 11,976 (14.8) & \\ Non-paid family worker & 5964 (72.0) & 2317 (28.0) & \\ Unemployed & 65,379 (78.3) & 18,112 (21.7) & \\ Marital status & & & \textless{}0.001 \\ Living with a spouse & 107,396 (80.2) & 26,457 (19.8) & \\ Separation from a spouse & 5132 (81.3) & 1177 (18.7) & \\ Bereavement & 18,300 (72.5) & 6933 (27.5) & \\ Divorce & 7570 (82.3) & 1626(17.7) & \\ Single & 32,344 (90.1) & 3564 (9.9) & \\ Experienced fever or cough symptoms in the & & & 0.014 \\ past 3 months & 1991 (83.1) & 406 (16.9) & \\ No & 168,847 (81.1) & 39,362(18.9) & \\ \hline \hline \end{tabular} * The significance level was calculated by the chi-square test.  
\end{table}
Table 2: \(Cont.\)  
Figure 3: Variable Importance of AdaBoost model for predicting COVID-19-induced anxiety in Korean adults (top 5 only).  
Developing a Nomogram for Predicting Anxiety Due to COVID-19 for Adults Living in South Korean Communities  
Figure 5 presents the nomogram for predicting anxiety due to COVID-19 for adults living in South Korean communities. Education level had the largest influence among factors related to anxiety due to COVID-19. When the education level was elementary school graduation, the risk of anxiety due to COVID-19 was the highest. For example, the developed logistic regression nomogram predicted that the risk of anxiety due to COVID-19 would be 63% for a female older adult who felt a lot of subjective stress, did not attend a middle school, was 70.6 years old, and thought that neighbors and colleagues responded to COVID-19 appropriately (Figure 5).  
This study presents the precision, recall, classification accuracy, F-1 score, AUC, and calibration plot to validate the prediction performance of the developed logistic regression nomogram (Figures 6-8). The result of the 5-fold cross validation showed that the classification accuracy of the nomogram for predicting anxiety due to COVID  
Figure 4: General accuracy of the AdaBoost-based model for predicting anxiety due to COVID-19.  
Figure 5: Anxiety prediction nomogram due to COVID-19 for South Korean adults (n = 210,606).