## 2 Materials and Methods
### AdaBoost  
The AdaBoost algorithm is used to create a strong classifier with improved performance by selecting classifiers with weak classification power. The AdaBoost algorithm is a process of creating a strong classifier by finding the best combination of week classifiers that can best distinguish an image of an object to be detected (positive sample) from other images (negative sample). It reduces the weight of samples that were correctly recognized by a weak classifier and increases the weight of samples that were incorrectly recognized to apply it to the next weak classifiers. The final strong classifier consists of a combination of weak classifiers, and the error rate decreases as the number of weak classifiers with small weighted errors increases. A weak classifier (\(\mathrm{g_{j}(x)}\)) is expressed as in Equation (1) by using a feature for a sample x, (\(\mathrm{f_{j}}\)), threshold (\(\mathrm{\theta_{j}}\)), and \(\mathrm{p_{j}}\) indicating parity. The classification result is expressed as 1 (positive sample) or 0 (negative sample). \[\mathrm{g_{j}(x)}=\left\{ \begin{array}{ll}1&\mathrm{if}\ \mathrm{p_{j}f_{j}(x)<p_{j} \mathrm{\theta_{j}}}\\ 0&\mathrm{otherwise}\end{array} \right. \tag{1}\]  
The threshold value (\(\mathrm{\theta_{j}}\)) is set to a value that maximizes the classification performance of weak classifiers. Moreover, if the feature values corresponding to the positive sample have a smaller value on average than those corresponding to the negative sample, \(\mathrm{p_{j}}\) (indicating the direction of a sign of inequality) equals to 1. In the opposite case, it is \(-1\). The following explains the process of the AdaBoost algorithm that selects weak classifiers with good classification performance, measures the importance of each, assigns weights, and combines them to create a strong classifier. Step 1 is to compose the sample data to learn. The number of sample data to learn is N, where \(\mathrm{x_{i}}\) represents the value of each sample data. \(\mathrm{y_{i}}\) is 1 for a positive sample and 0 for a negative sample according to class information corresponding to each sample. The equation is presented in Equation (2). \[(\mathrm{x_{1}},\mathrm{y_{1}}),\ \ldots,\ (\mathrm{x_{N}},\mathrm{y_{N}}),\ \mathrm{x_{i}}\in\mathrm{R^{d}},\ \mathrm{y_{i}}\in\{0,1\}. \tag{2}\] \begin{table}
\begin{tabular}{l l} \hline \hline
**Categories** & **Variables** \\ \hline Sociodemographic characteristics & Household type, basic living security recipient status, mean monthly household income, etc. \\ Smoking & Lifetime smoking, current smoking, smoking cessation attempt, and \\  & secondhand smoke \\  & Lifetime drinking, drinking frequency, drinking amount, and binge \\  & drinking frequency \\  & Wearing seat belts, experience of driving while intoxicated, etc. \\  & High-intensity physical activity, moderate-intensity physical activity, walking, etc. \\  & Number of breakfasts, nutrition label recognition, reading comprehension, \\  & and utilization \\  & Obesity and weight control \\  & Oral health \\  & Vaccinations and physical examination \\  & Influenza vaccination \\  & Early recognition of stroke symptoms, early recognition of myocardial infarction, \\  & hypertension, diabetes, etc. \\  & Medical use \\  & Ummet medical need experience \\  & Accidents and Addiction \\  & Accident and addition \\  & and addition \\  & Subjective health status \\  & Social and physical environment \\  & Cardiopulmonary resuscitation (CPR) \\  & CPR awareness, CPR training experience, etc. \\  & Education and economic activity \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Description of explanatory variables. Step 2 is to initialize all samples by giving the same weight. The initial weight for the \(i\)-th sample is calculated by Equation (3). \[\mathrm{w_{1,i}}=\left\{ \begin{array}{rl}\frac{1}{2\mathrm{m}}\ \mathrm{if}\ \mathrm{y_{i}}=0\\ \frac{1}{2\mathrm{m}}\ \mathrm{if}\ \mathrm{y_{i}}=1\end{array} \right.