# Application of Artificial Neural Network to Somatotype Determination
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Somatotypes and ANN Form** & **No.** & \multicolumn{6}{c}{**Weights and Biases**} \\ \hline MLP 4-4-1 & \(i\) & \(D_{1i}\) & \(D_{2i}\) & \(D_{3i}\) & \(D_{4i}\) & \(D_{5i}\) & \(W_{i}\) & \(W_{b}\) \\  & 1 & \(-2.5196\) & 3.3665 & \(-0.1069\) & 0.2737 & \(-2.0654\) & \(-0.0124\) & \\ Endo & 2 & \(-1.6057\) & 0.6506 & \(-1.8208\) & 0.2305 & 1.2134 & \(-0.8628\) & \\  & 3 & 0.7439 & \(-1.2159\) & \(-0.8333\) & 0.0953 & 2.2781 & \(-0.1618\) & \\  & 4 & 2.2264 & 2.1972 & 1.2505 & 2.5316 & 3.9790 & \(-0.5781\) & \\  & 1 & 1.8162 & 1.4232 & \(-1.8635\) & \(-0.2445\) & \(-0.7219\) & 1.4688 & \\ Meso & 2 & 0.5244 & \(-2.2078\) & \(-0.5086\) & 1.9776 & 1.0502 & 1.3685 & \(-0.2921\) \\  & 3 & \(-1.3144\) & 0.0394 & 0.8123 & \(-0.6986\) & \(-0.6232\) & 1.0849 & \(-0.2921\) \\  & 4 & \(-0.9115\) & 0.1162 & \(-3.0057\) & 0.6690 & \(-1.8619\) & \(-1.1589\) & \\ MLP 2-4-3 & & \(D_{1i}\) & \(D_{2i}\) & \(D_{3i}\) & \(W_{\text{Endo}\ i}\) & \(W_{\text{Etoo}\ i}\) & \(W_{\text{Meso}\ i}\) & \(W_{b\text{Endo}}\) \\  & 1 & 1.5266 & \(-0.0379\) & 0.2213 & \(-1.1164\) & & & \\ Endo, Eto, and Meso & 2 & 2.0005 & 1.5189 & \(-0.7945\) & 0.0018 & & & \\  & 3 & 3.1287 & \(-5.8295\) & \(-2.1418\) & \(-0.1264\) & \(-0.1439\) & \(-0.0378\) & \(-0.1789\) \\  & 4 & \(-2.0762\) & 1.5257 & 0.8236 & \(-0.2852\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weights and biases between input and hidden layers and between hidden and output layers for artificial neural network (ANN) Endo, Eto, and Meso somatotypes. 0.02) and MLP 6-5-1 (RMSE greater by 0.16). The Etoo somatotype calculated from MLP 4-2-3 (RMSE = 0.62) gives worse results than that calculated from MLP 4-3-1 (RMSE greater by 0.16), from MLP 6-4-3 (RMSE greater by 0.1), and from MLP 6 -5-1 (RMSE greater by 0.24). The Meso somatotype calculated from MLP 4-2-3 (RMSE = 0.77) gives slightly worse results than that calculated from MLP 4-4-1 (RMSE greater by 0.02), from MLP 6-4-3 (RMSE greater by 0.09) and worse than that calculated from MLP 6-5-1 (RMSE greater by 0.16). In our study group the mean somatotype was 3.6-2.9-2.9 by the Heath-Carter method and 3.6-2.9-2.9 by ANN MLP 2-4-3 (Table 4), that indicates on mesomorphic-endomorph somatotype. The study of [PERSON] and [PERSON] [23] with BIA-based formulas found that in Russian a adult meso-endomorphic type prevailed in females aged 16-86 years, and somatotype was age-related. They also noted age- and sex-related patterns of somatotype changes. The endomorph somatotype is dominant in the average woman, especially if she is not involved in sports while in men the mesomorph dominates. Many studies confirm that women are predominantly endomorphic with a secondary mesomorphic component [48; 49; 50]. Figure 14 presents Scatterplot of Etoo, Endo and Meso somatotypes and levels of agreement graph from Bland-Altman analysis. Bland-Altman 95% limits of agreement for somatotype determination using the Heath-Carter method and ANNs were \(-\)0.26 (95% confidence interval (CI): \(-\)1.43, 0.91) and 0.01 (95% CI: \(-\)1.21, 1.19) for Meso (MLP 4-4-1 and MLP 2-4-3, respectively), \(-\)0.21 (95% CI: \(-\)1.47, 1.05) and 0.06 (CI: \(-\)1.02, 1.14) for Endo (MLP 4-4-1 and MLP 2-4-3, respectively) and 0.03 (CI: \(-\)0.77, 0.83) for Etoo (MLP 2-4-3). The differences between the values of each somatotype calculated using the Heath-Carter method and ANNs are statistically insignificant (Table 4). Results of the above statistical analyses proved that somatotype rating by ANNs has a high agreement with somatotype determination by the Heath-Carter method and, as mentioned above, BIA data are easier to obtain than anthropometric measurements. To determine factors (ANN input data), which most affect the somatotypes predicted by ANN, a sensitivity analysis was performed.