## 5 Discussion and Future Work  
The purpose of this study was to investigate the optimal missing value imputation and statistical methods for estimating demographic features through anthropometric measurements. We examined general imputation methods with machine learning algorithms to estimate sex and stature using anthropometric measurements related to the upper and lower limbs. In this study, we proposed three ways to impute missing values, and within our classification analysis of machine learning, we used seven classes to classify statures. Estimates of this class are significant for constructing biometric profiles of humans using various kinds of anthropometric data. In addition, this study has provided a baseline of comparison to researchers who conduct study that estimates human biological information based on anthropometric measurements by country and ethnicity. First, we confirmed through upper and lower limbs that there were differences in the accuracy of the stature estimates for Korean males versus females; specifically, the stature estimates for the men were more accurate than the estimates of Korean women's stature. Previous researchers obtained similar results for Koreans [10; 45], but these results were not unique to Koreans: Other researchers found the same results in multiple studies on estimating stature across different countries [4; 5; 48; 49; 50]. Second, through our experiments on imputing missing values, we confirmed that multiple imputation was the most accurate in all cases of estimating biological information based on the upper and lower limbs except for high missing ratios. The multiple imputation used in this study was estimated using Gaussian distribution, which imputes missing data based on covariance between data. Therefore, this method is potentially more accurate than others but has a disadvantage in that it requires a larger minimum data set than do other methods for estimating the parameters of Gaussian distribution. We confirmed similar results in this study: As the missing ratio increased, the accuracy of multiple imputations decreases rapidly; when the missing ratio was over 0.7, mean imputation showed the highest accuracy in all cases. With mean imputation, the missing data are estimated based on the averages of the totals without considering relationships among features, so that the average for each missing ratio in the overall data does not change significantly. The other imputation methods, multiple and nearest neighbor imputations can be overfitted to the small amount of observed information. In this study, among the three imputation methods, the accuracy of mean imputation decreased the least when the missing ratio increased. Therefore, when estimating stature through anthropometric measurements in Koreans, if the victim's body is severely damaged and it is difficult to obtain measurements for each body part, anthropometric measurements should be calculated using mean imputation. Third, from the perspective of the learning algorithm, we used two types of linear classification (logistic, NB) and two types of nonlinear classification (SVM, ANN) in this study. In the previous studies of estimating stature through anthropometric data, researchers primarily performed linear regression and classification analysis based on the linearity of the human body. However, recently researchers have confirmed the accuracy of estimating stature using nonlinear or machine learning methods [51]. We also determined that in the context of missing data, which is the main contribution of this study, nonlinear classification was more accurate for measuring stature. Therefore, it is necessary to expand the methodology based on machine learning in research to estimate or classify biological information of humans through anthropometry. The limitation of this study and future research are as follows.