## 4 Results
### _Stature Classification: Lower Limb_
This was because the accuracy of multiple imputation decreased more than did accuracy with the other two methods when the missing ratio increased. The highest accuracy among the four machine learning algorithms was with SVM: 0.837; missing ratio = 0.2. Second, with females, when the missing ratio was 0.2 to 0.6, multiple imputation using SVM showed the highest accuracy, and when the missing ratio was 0.5 or 0.6, accuracy was the same for SVM and ANN. In contrast, when the missing ratio was 0.7 to 0.8, mean imputation using SVM showed the highest accuracy. Among all methods, NN imputation showed the lowest accuracy at all missing ratios. Finally, in the case of males, when the missing ratio was 0.2 to 0.5, multiple imputation using SVM showed the highest accuracy. Unlike with the female cases, when the missing ratio was 0.5, the accuracy \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sex**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Imputation**} & \multicolumn{8}{c}{**Missing Ratio**} \\ \cline{3-10}  & & & **0.2** & **0.3** & **0.4** & **0.5** & **0.6** & **0.7** & **0.8** \\ \hline \multirow{8}{*}{Combined sexes sexes} & Logistic & Mean & 0.778 & 0.757 & 0.735 & 0.710 & 0.679 & 0.643 & 0.596 \\  & & NN & 0.694 & 0.624 & 0.559 & 0.500 & 0.454 & 0.433 & 0.427 \\  & Multiple & 0.816 & 0.805 & 0.788 & 0.756 & 0.702 & 0.615 & 0.497 \\ \cline{2-10}  & NB & Mean & 0.784 & 0.772 & 0.757 & 0.737 & 0.709 & 0.668 & 0.613 \\  & & NN & 0.709 & 0.655 & 0.599 & 0.543 & 0.493 & 0.457 & 0.428 \\  & & Multiple & 0.800 & 0.790 & 0.770 & 0.734 & 0.668 & 0.570 & 0.451 \\ \cline{2-10}  & SVM & Mean & 0.822 & 0.811 & 0.796 & 0.777 & 0.752 & 0.716 & 0.668 \\  & & NN & 0.755 & 0.700 & 0.638 & 0.573 & 0.519 & 0.486 & 0.493 \\  & Multiple & 0.837 & 0.824 & 0.803 & 0.767 & 0.706 & 0.617 & 0.501 \\ \cline{2-10}  & ANN & Mean & 0.811 & 0.798 & 0.777 & 0.761 & 0.732 & 0.689 & 0.636 \\  & & NN & 0.770 & 0.722 & 0.657 & 0.579 & 0.511 & 0.475 & 0.477 \\  & Multiple & 0.835 & 0.822 & 0.802 & 0.767 & 0.707 & 0.617 & 0.498 \\ \hline \multirow{8}{*}{Female} & Logistic & Mean & 0.757 & 0.738 & 0.715 & 0.696 & 0.669 & 0.637 & 0.596 \\  & NN & 0.690 & 0.624 & 0.568 & 0.520 & 0.485 & 0.462 & 0.456 \\  & Multiple & 0.791 & 0.784 & 0.768 & 0.740 & 0.690 & 0.612 & 0.513 \\ \cline{2-10}  & NB & Mean & 0.759 & 0.747 & 0.729 & 0.705 & 0.674 & 0.631 & 0.574 \\  & NN & 0.701 & 0.653 & 0.603 & 0.558 & 0.513 & 0.477 & 0.440 \\  & Multiple & 0.771 & 0.762 & 0.743 & 0.710 & 0.649 & 0.557 & 0.462 \\ \cline{2-10}  & SVM & Mean & 0.788 & 0.775 & 0.758 & 0.740 & 0.718 & 0.682 & 0.637 \\  & NN & 0.732 & 0.680 & 0.629 & 0.576 & 0.533 & 0.504 & 0.496 \\  & Multiple & 0.808 & 0.797 & 0.778 & 0.745 & 0.692 & 0.614 & 0.517 \\ \cline{2-10}  & ANN & Mean & 0.771 & 0.751 & 0.730 & 0.709 & 0.681 & 0.647 & 0.603 \\  & NN & 0.719 & 0.668 & 0.611 & 0.558 & 0.516 & 0.487 & 0.477 \\  & Multiple & 0.806 & 0.795 & 0.776 & 0.745 & 0.692 & 0.613 & 0.514 \\ \hline \multirow{8}{*}{Male} & Logistic & Mean & 0.770 & 0.749 & 0.726 & 0.703 & 0.674 & 0.639 & 0.594 \\  & NN & 0.683 & 0.610 & 0.547 & 0.493 & 0.459 & 0.440 & 0.443 \\  & Multiple & 0.813 & 0.804 & 0.787 & 0.756 & 0.702 & 0.618 & 0.512 \\ \cline{2-10}  & NB & Mean & 0.776 & 0.762 & 0.741 & 0.718 & 0.684 & 0.639 & 0.577 \\  & NN & 0.707 & 0.655 & 0.602 & 0.552 & 0.505 & 0.469 & 0.439 \\  & Multiple & 0.792 & 0.781 & 0.762 & 0.724 & 0.659 & 0.565 & 0.465 \\ \cline{2-10}  & SVM & Mean & 0.808 & 0.793 & 0.776 & 0.757 & 0.732 & 0.693 & 0.651 \\  & NN & 0.748 & 0.693 & 0.637 & 0.581 & 0.533 & 0.501 & 0.501 \\  & Multiple & 0.833 & 0.819 & 0.798 & 0.763 & 0.705 & 0.621 & 0.519 \\ \cline{2-10}  & ANN & Mean & 0.788 & 0.768 & 0.75 & 0.724 & 0.694 & 0.657 & 0.609 \\  & NN & 0.748 & 0.693 & 0.631 & 0.567 & 0.517 & 0.485 & 0.480 \\  & Multiple & 0.830 & 0.817 & 0.796 & 0.763 & 0.706 & 0.621 & 0.515 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Accuracy results by imputation method and learning algorithm: Lower limb.