## 2 Materials and Methods
### Data Collection  
The dataset included 41 variables encompassing demographic information, clinical measurements, lifestyle factors, and cardiovascular outcomes. The key features used in this study were as follows (Tables 1 and 2). The dependent variable is defined as the presence or absence of mortality risk (1 = Yes, 0 = No), and the machine-learning model treats it as a classification problem. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Variable Name** & **English Description** \\ \hline Basic Information & RANDID & Random ID for individual identification \\  & SEX & Sex (1 = Male, 2 = Female) \\  & AGE & Age (years) \\ \hline Health Status and Risk Factors & TOTCHOL & Total cholesterol (mg/dL) \\ \hline  & SYSBP & Systolic blood pressure (mmHg) \\ \hline  & DIABP & Diastolic blood pressure (mmHg) \\ \hline  & CURSMOKE & Current smoking status (1 = Yes, 0 = No) \\ \hline  & CIGPDAY & Cigarettes per day \\ \hline  & BMI & Body mass index (BMI, kg/m2) \\ \hline  & DIABETES & Diabetes (1 = Yes, 0 = No) \\ \hline  & BPMEDS & Antihypertensive medication (1 = Yes, 0 = No) \\ \hline  & HEARTRTE & Heart rate (bpm) \\ \hline  & GLUCCOSE & Glucose level (mg/dL) \\ \hline  & HDLC & High-density lipoprotein cholesterol (mg/dL) \\  & LDLC & Low-density lipoprotein cholesterol (mg/dL) \\ \hline  & Medical History & educ & Education level \\ \hline  & PREVCHD & Previous coronary heart disease (1 = Yes, 0 = No) \\ \hline  & PREVAP & Previous angina pectoris (1 = Yes, 0 = No) \\ \hline  & PREVMI & Previous myocardial infarction (1 = Yes, 0 = No) \\ \hline  & PREVSTRK & Previous stroke (1 = Yes, 0 = No) \\  & PREVHYP & Previous hypertension (1 = Yes, 0 = No) \\ \hline  & DEATH & Death (1 = Yes, 0 = No) \\ \hline  & ANGINA & Angina occurrence (1 = Yes, 0 = No) \\ \hline  & HOSPMI & Hospitalization for myocardial infarction (1 = Yes, 0 = No) \\ \hline  & MI\_FCHD & Myocardial infarction or coronary heart disease \\  & & occurrence (1 = Yes, 0 = No) \\ \hline  & ANYCHD & Any coronary heart disease occurrence (1 = Yes, 0 = No) \\ \hline  & STROKE & Stroke occurrence (1 = Yes, 0 = No) \\ \hline  & CVD & Cardiovascular disease occurrence (1 = Yes, 0 = No) \\ \hline  & HYPERTEN & Hypertension occurrence (1 = Yes, 0 = No) \\ \hline  & & \\ \end{tabular}
\end{table} 
Table 1: Categories, descriptions, and notations for each variable in FHS. Among these variables, DEATH was used as the primary outcome variable, representing all-cause mortality. Independent variables included age, sex, blood pressure (SYSBP and DIABP), cholesterol levels (TOTCHOL, HDLC, and LDLC), glucose levels (GLUCCSE), smoking status (CURSMOKE and CIGPDAY), body mass index (BMI), diabetes status (DIABETES), and medication use (BPMEDS). Data preprocessing was conducted systematically to ensure the quality and consistency of the dataset before model training. One of the critical steps involved handling missing values using multiple imputation techniques. Instead of discarding incomplete records, which could introduce bias or reduce the sample size, we applied multiple imputation to estimate missing values based on observed data patterns. Specifically, we employed the Multivariate Imputation by Chained Equations (MICE) method, which iteratively predicts missing values for each variable using regression models trained on the available data. This approach preserves the underlying relationships between variables and minimizes the risk of bias introduced by missingness. For continuous variables, standardization was performed to ensure comparability across different scales. Many machine-learning algorithms, particularly those based on gradient descent (e.g., logistic regression), are sensitive to differences in scale. To address