## 4 Results
### _Stature Classification: Lower Limb_
\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} & \multirow{4}{*}{Logistic} & Mean & 0.721 & 0.700 & 0.680 & 0.654 & 0.628 & 0.584 & 0.549 \\  & & Nearest & & & & & & & \\  & & neighbor & 0.657 & 0.594 & 0.539 & 0.487 & 0.445 & 0.417 & 0.407 \\  & & (NN) & & & & & & & \\  & & Multiple & 0.745 & 0.732 & 0.713 & 0.684 & 0.643 & 0.579 & 0.493 \\ \cline{2-10}  & \multirow{4}{*}{Naive Bayes classifier (NB)} & Mean & 0.666 & 0.660 & 0.650 & 0.634 & 0.614 & 0.581 & 0.537 \\  & & NN & 0.622 & 0.586 & 0.550 & 0.508 & 0.465 & 0.432 & 0.398 \\  & & Multiple & 0.662 & 0.650 & 0.633 & 0.605 & 0.563 & 0.510 & 0.436 \\ \cline{2-10}  & \multirow{4}{*}{Support vector machine (SVM)} & Mean & 0.749 & 0.734 & 0.717 & 0.696 & 0.671 & 0.638 & 0.595 \\  & & NN & 0.698 & 0.647 & 0.597 & 0.539 & 0.491 & 0.452 & 0.445 \\  & & Multiple & 0.756 & 0.741 & 0.719 & 0.688 & 0.644 & 0.582 & 0.493 \\ \cline{2-10}  & \multirow{4}{*}{Artificial neural network (ANN)} & Mean & 0.734 & 0.714 & 0.697 & 0.670 & 0.642 & 0.609 & 0.565 \\  & & NN & 0.692 & 0.642 & 0.586 & 0.526 & 0.476 & 0.439 & 0.432 \\  & & Multiple & 0.753 & 0.738 & 0.717 & 0.687 & 0.644 & 0.582 & 0.495 \\ \hline \multirow{8}{*}{Female} & Logistic & Mean & 0.681 & 0.661 & 0.642 & 0.622 & 0.596 & 0.566 & 0.528 \\  & & NN & 0.625 & 0.576 & 0.529 & 0.486 & 0.457 & 0.432 & 0.420 \\  & & Multiple & 0.699 & 0.686 & 0.663 & 0.634 & 0.591 & 0.535 & 0.466 \\ \cline{2-10}  & \multirow{4}{*}{NB} & Mean & 0.644 & 0.634 & 0.620 & 0.597 & 0.570 & 0.533 & 0.479 \\  & & NN & 0.600 & 0.572 & 0.536 & 0.501 & 0.464 & 0.434 & 0.406 \\  & & Multiple & 0.635 & 0.619 & 0.594 & 0.559 & 0.509 & 0.453 & 0.405 \\ \cline{2-10}  & \multirow{4}{*}{SVM} & Mean & 0.697 & 0.682 & 0.665 & 0.645 & 0.622 & 0.591 & 0.551 \\  & & NN & 0.652 & 0.611 & 0.569 & 0.525 & 0.486 & 0.454 & 0.440 \\  & & Multiple & 0.707 & 0.691 & 0.668 & 0.638 & 0.595 & 0.539 & 0.469 \\ \cline{2-10}  & \multirow{4}{*}{ANN} & Mean & 0.684 & 0.664 & 0.645 & 0.625 & 0.600 & 0.571 & 0.532 \\  & & NN & 0.635 & 0.590 & 0.545 & 0.506 & 0.470 & 0.441 & 0.429 \\  & & Multiple & 0.703 & 0.689 & 0.665 & 0.636 & 0.594 & 0.538 & 0.467 \\ \hline \multirow{8}{*}{Male} & Logistic & Mean & 0.729 & 0.710 & 0.686 & 0.662 & 0.637 & 0.605 & 0.562 \\  & & NN & 0.658 & 0.596 & 0.537 & 0.488 & 0.451 & 0.431 & 0.425 \\  & & Multiple & 0.757 & 0.744 & 0.727 & 0.699 & 0.658 & 0.601 & 0.514 \\ \cline{2-10}  & \multirow{4}{*}{NB} & Mean & 0.696 & 0.686 & 0.669 & 0.648 & 0.620 & 0.580 & 0.519 \\  & & NN & 0.651 & 0.615 & 0.573 & 0.531 & 0.488 & 0.451 & 0.420 \\  & & Multiple & 0.692 & 0.679 & 0.659 & 0.627 & 0.583 & 0.528 & 0.458 \\ \cline{2-10}  & \multirow{4}{*}{SVM} & Mean & 0.734 & 0.715 & 0.695 & 0.671 & 0.645 & 0.614 & 0.573 \\  & & NN & 0.688 & 0.640 & 0.585 & 0.532 & 0.484 & 0.448 & 0.438 \\  & & Multiple & 0.762 & 0.748 & 0.729 & 0.700 & 0.660 & 0.602 & 0.517 \\ \cline{2-10}  & \multirow{4}{*}{ANN} & Mean & 0.749 & 0.733 & 0.717 & 0.695 & 0.673 & 0.643 & 0.604 \\  & & NN & 0.704 & 0.658 & 0.607 & 0.555 & 0.510 & 0.468 & 0.453 \\ \cline{2-10}  & \multirow{4}{*}{NN} & Mean & 0.760 & 0.746 & 0.728 & 0.700 & 0.660 & 0.603 & 0.518 \\ \cline{2-10}  & \multirow{4}{*}{NN} & Mean & 0.749 & 0.733 & 0.717 & 0.695 & 0.673 & 0.643 & 0.604 \\ \cline{2-10}  & & NN & 0.704 & 0.658 & 0.607 & 0.555 & 0.510 & 0.468 & 0.453 \\ \cline{2-10}  & & Multiple & 0.760 & 0.746 & 0.728 & 0.700 & 0.660 & 0.603 & 0.518 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Accuracy results by imputation method and learning algorithm: Upper limb. All learning algorithms were less accurate as the missing ratio increased, although when the missing ratio was 0.5 or more, mean imputation was more accurate than multiple imputation.