## 4 Data Description and Learning Procedure  
We applied Char-RNN to data from the KNHANES VI (2013, 2014, and 2015) to predict five chronic diseases (hypertension, stroke, myocardial infarction, angina pectoris, and diabetes mellitus) with the greatest influence on comorbidities. The KNHANES is a national health survey conducted annually by the Korea Center for Disease Control that consists of questions to examine characteristics such as health behavior, nutritional intake, and chronic diseases [36]. The survey is administered to participants selected at the city, province, and county level. The screening items included in the survey are selected by the sector advisory committees and the coordinating advisory council. The KNHANES datasets have high reliability and accuracy because the data are collected by a national institute and the survey items are revised during each phase of the survey. The KNHANES datasets consist of 760 variables. This study focused on the phase VI data, containing approximately 22,000 cases reflecting the most recent lifestyle habits and patterns available in the datasets. We selected five popular chronic diseases among a variety of diseases and related variables, including osteoarthritis, rheumatoid arthritis, osteoporosis, tuberculosis, asthma, thyroid disease, cancer, inflammation, and hepatitis. Table 1 presents the gender and age composition of the subjects included in the dataset for this study. Although the dataset contained 22,000 cases, most individual cases did not have any diagnosed disease. Consequently, significant variables were selected from the dataset with 760 variables using a regression analysis with stepwise variable selection. A total of 62 variables were selected for the five diseases. Correlations among selected variables were used to remove variables with strong correlation, with a correlation coefficient of 0.6 or higher, and finally, 32 variables were used for analysis. To maximize analytical accuracy, we excluded selected variables that coexisted with other selected variables during data processing (Table 2). After variable selection, each variable value was interpreted as text and converted to character format. As shown in Figure 1, the numerical values of 29 variables relevant to the five diseases were converted to letters using the following rule: (0 \(\rightarrow\) a, 1 \(\rightarrow\) b, 2 \(\rightarrow\) c... ). The missing values were replaced with tabs that could not be represented in alphabetical order. The reason for assigning numbers to one alphabet was to train the data in the form of one sentence. The sequences 0001 and AAAB are the same. \begin{table}
\begin{tabular}{c c} \hline
**DF2\_ag** & **DI4\_pr** & **DI6\_ag** \\ \hline
888 & 8 & 888 \\
888 & 1 & 888 \\
888 & 8 & 888 \\ \hline \end{tabular}
\end{table} 
Table 2: Data construct. DF2_ag, time of depression diagnosis; DI6_ag, time of angina pectoris diagnosis; DI4_pr, presence or absence of comorbidities of myocardial infarction and angina pectoris. \begin{table}
\begin{tabular}{c c} \hline
**Age** & **1-9: 2489 (10.8\%)** \\
**10-18: 2425 (10.6\%)** \\
**19-29: 2250 (9.8\%)** \\
30-39: 2946 (12.8\%) \\
**40-49: 3283 (14.3\%)** \\
**50-59: 3499 (15.2\%)** \\
60-69: 3014 (13.1\%)** \\
**70 and older: 3042 (13.3\%)** \\ \hline Gender variable & Male: 10.411 (45.4\%) \\ Female: 12.537 (54.6\%) \\ \hline Type of residential area & Neighborhood: 18,551 (80.8\%) \\  & Town/township: 4397 (19.2\%) \\ \hline \multirow{2}{*}{Marital status (for those aged 30 or older)} & Not married: 904 (5.7\%) \\  & Married (with a spouse): 12,298 (78.1\%) \\  & Married (widowed, divorced, or separately: 2549 (16.2\%) \\ \hline \multirow{2}{*}{Education level (for those aged 30 or older)} & Graduated from elementary school or less: 3670 (27.2\%) \\  & Graduated from middle school: 1609 (11.9\%) \\ \cline{1-1}  & Graduated from high school: 4135 (30.6\%) \\ \cline{1-1}  & Graduated from college or higher: 4084 (30.3\%) \\ \hline \end{tabular}
\end{table}