## 5 Experimental Results  
The analysis was performed using two deep learning models. Figure 2 shows the whole process. Each model was trained to make decisions regarding cases labeled as normal, hypertension, stroke, myocardial infarction, angina pectoris, or diabetes mellitus and tested on test datasets with missing information to classify new cases. Learning was conducted with approximately 600 cases per label. The number of cases of angina pectoris and myocardial infarction was reduced to fewer than 600 during data processing; therefore, it can be seen that they are lower than the other chronic diseases. Hence, the sizes of these groups were increased by replicating existing cases to prevent overfitting due to imbalanced data. Char-RNN required the value of each case to be text, thus the data were converted using the rule (0 \(\rightarrow\) a, 1 \(\rightarrow\) b, 2 \(\rightarrow\) c... ). The data format was text separated by tabs. The data were transformed to sentence format for the learning phase. Stepwise regression was used to remove the KNHANES VI variables that did not influence the five selected chronic diseases. There were a total of 760 variables in KNHANES VI, and 652 were selected after excluding pediatric- and female-specific and cancer-, joint-, or dental-related variables. Next, variables that were significantly associated with the five chronic diseases were selected using a stepwise selection method. In this case, variables with a p-value less than a significance level of 0.05 were extracted, and the rest were excluded because they were greater than that level. As a result, 17 variables were selected for hypertension, 20 for stroke, 23 for myocardial infarction, 22 for angina pectoris, and 29 for diabetes mellitus (Table A1). A few influential variables were associated with  
Figure 1: Data category configuration. Figure 2: Overview of proposed procedure using character-recurrent neural network (Char-RNN). more than one disease; therefore, a total of 62 unique variables were selected. Table 10 lists detailed descriptions of the selected variables shown in Table 11, along with the variables affecting the five diseases, and detailed descriptions of the variables came with the KNHANES VI guideline. Of the available 22,000 cases, those with missing values in 200 or more variables relevant to the five chronic diseases were completely removed from the analysis dataset. In addition, the existing training data had to be cleaned up to make the model using the data of the selected variables. Therefore, we removed the data where there was at least one missing value for each disease. Finally, approximately 3000 cases were selected after the filtering steps. The analysis outcome can be affected by the presence of correlations among the selected variables; hence, a correlational analysis was conducted. The results show that a few variables were correlated with others, and those that strongly correlated with included variables were removed. The criterion of r > 0.6 was used to select and remove strongly correlated variables. A total of 32 variables remained after strongly correlated variables were removed. Next, three variables (time of depression diagnosis, DF2_ag; time of angina pectoris diagnosis, DI6_ag; and presence or absence of comorbidities of myocardial infarction and angina pectoris, DI4_pr) were removed because they had similar values across all five chronic diseases and were likely to reduce the analysis accuracy. The response rates for these three variables were very low because a large majority of respondents did not know the answer. Consequently, the value of DF2_ag, DI4_pr, and DI6_ag was mostly 8, which was used to code the response \"do not know\" (Figure 2). Such variables may reduce the analysis accuracy; thus, they were removed, and the analysis was performed on the final set of 29 variables.