## 3 Results and Discussion
### Prediction of Risk Factors for Dyslipidemia Based on Health Behavior by Age  
A decision tree analysis was performed to examine the combination of dyslipidemia risk factors according to health behavior by age. A decision tree analysis has the advantage of being able to intuitively examine the combination of factors with the highest level classified as the dependent variable for dyslipidemia and the conditions of independent variables that classify dyslipidemia. Figure 4 shows the results of the decision tree analysis for the entire sample of 12,025 people. In addition to the general characteristics of the subjects, 19 variables were used in the analysis, including variables related to health behavior, and the max_depth was set to 5 to specifically examine the prediction results of each explanatory variable classified as dyslipidemia. The model's train accuracy was 0.987, and the test accuracy was 0.830, but cross-validation was performed to avoid overfitting, which may occur during the model learning process and to evaluate the model reliably. In this study, the KFold cross-validation method was used to create a general model through multiple validations and tune the parameters. As a result of performing parameter tuning and cross-validation using the GridSearchCV function, the best performance was achieved when the tuned parameters were max_depth = 5, min_samples_split = 3, and splitter = random. In addition, in order to find the optimal max_depth in cross-validation using KFold, we fitted the random forest model from 1 to the number of sample variables. As a result, the average accuracy was 0.901 when max_depth = 5 and 0.899 when max_depth = 6, and it was confirmed that the average accuracy was the highest when max_depth = 5. Figure 4: Decision tree for predicting risk factors for dyslipidemia (regardless of age). The cross-validation average was 0.961, so the accuracy of the classification model can be expected to be approximately 96%. The objective function that determines the separation criteria in the decision tree uses the chi-square statistic, Gini index, and entropy as measures of impurity. The closer the entropy is to 0, the lower the impurity is, and if the impurity is 0, this means that there is only one data point in one category. \[\chi^{2}=\sum_{k=1}^{m}\left(E_{k}-O_{k}\right)^{2}/E_{k} \tag{2}\]  
Equation (2) is the calculation formula for the chi-square statistic. \(E\) and \(O\) are the expected frequency and the observed frequency, respectively, and the separation criterion is set to the value at which the chi-square statistic is maximized. \[\mathrm{G}=1-\sum_{k=1}^{m}\left(p_{k}\right)^{2} \tag{3}\]  
In Equation (3), \(P_{K}\) is the proportion of data belonging to class \(k\). \[\mathrm{G}=\sum_{i=1}^{d}R_{i}\Bigg{(}1-\sum_{k=1}^{m}\left(p_{k}\right)^{2} \Bigg{)} \tag{4}\]  
In Equation (4), \(R_{i}\) is the proportion after splitting. In this study, the Gini coefficient was used as a measure of impurity, and its value becomes smaller when one class (no dyslipidemia) is more numerous than another class (dyslipidemia), which means that impurity is minimized [18; 19]. The results of classifying the risk factors for dyslipidemia in all of the 12,025 subjects regardless of age are shown in Figure 4. The higher the node formed first, the more important the variable is in terms of influence factors. As shown in Figure 4, the Gini coefficients of all of the nodes are less than 0.5, indicating good impurity. Regarding the factors that influenced the classification of the dependent variable, a dyslipidemia diagnosis, in all of the 12,025 subjects, those for the 19-34 and 35-49 age groups included age, family history, gender, and obesity, while health behaviors and eating habits did not have a significant effect. On the other hand, age was found to be the most important factor in the 50-64 and 65 and older groups, and other significant predictors were health awareness, obesity, and gender, in that order.