# Application of Artificial Neural Network to Somatotype Determination
## 3 Results
Results of sensitivity analysis of ANNs are presented in Table 2.  
Sensitivity analysis of ANNs shows that for MLP 4-4-1 the RMR (the largest R of 0.84, the greatest values of errors: RMSE = 0.74 and \(\chi^{2}\) = 0.83), Reac, and Res have the smallest impact, whereas BMI (the smallest R = 0.47 and the greatest errors: RMSE = 1.14 and \(\chi^{2}\) = 1.98) has the greatest impact on the value of the determined Endo somatotype. The smallest impact on the Meso somatotype form MLP 4-4-1 has Res (the greatest R of 0.81, the smallest errors: RMSE = 0.83 and \(\chi^{2}\) = 1.04) and TBW) while the greatest impact are from FFM and BMI, respectively (R is 0.55 and 0.61, RMSE: 1.13 and 1.09, \(\chi^{2}\) = 1.96 and 1.83, respectively). The ANN MLP 2-4-3 for determining all somatotypes is characterized by the fact, that the smallest impact on the value of the somatotypes has Res (the greatest R of 0.83, the smallest RMSE = 0.78 and \(\chi^{2}\) = 1.19), whereas the greatest impact has BMI (R = 0.26, RMSE = 1.35, \(\chi^{2}\) = 2.07). An analogous relationship also exists for each somatotype separately. This is to our knowledge the first study that specifically develops and applies ANNs to predictive equations for young women and opts for using also raw BIA variables to \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Somatotype** & **Heath-Carter Method** & **ANN MLP 4-4-1** & **ANN MLP 2-4-3** & _p-Value_ \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **_p-Value_** \\ \hline Endo & 3.63 \(\pm\) 1.28 a ** & 3.83 \(\pm\) 0.87 a ** & 3.55 \(\pm\) 1.12 a ** & ns \\ Meso & 2.86 \(\pm\) 1.34 b ** & 3.19 \(\pm\) 1.17 b ** & 2.94 \(\pm\) 1.11 b ** & ns \\ Etoo & 2.89 \(\pm\) 1.35 c ** & -1 & 2.90 \(\pm\) 1.39 c ** & ns \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Mean values (with standard deviation) of the somatotype in woman calculated from the Heath–Carter method and ANNs (the same letters indicate homogenous groups (\(\alpha\) < 0.05)). estimate somatotype. Due to the lack of results using ANNs for somatotypes in existing literature, discussion of our result is not possible. Therefore, we decided to test the accuracy of existing BIA-based formulas (Table A1) using our data. In recent years [51] using raw BIA-data have gained attention due to their association not only with FFM, but also markers with extracellular/intracellular distribution of water, BMC, integrity and muscle quality. Also, raw BIA-data as resistance (R), reactance (Xc) and phase angle (PhA) could be treated as indices of biological variables [52]. A few existing BIA-based formulas to calculate endo- and mesomorphy ratings [19; 20; 21; 22; 23; 24] based on the value of resistance at 50 kHz, BMI, body weight, fat mass and fat free mass, as well as gender and age were considered. Taking into account the age of our group, we could use only two existing BIA-based formulas [17; 23] for endo- and mesomorphy. The statistical results obtained (Table A1) by using equations to assess endo- and mesomorphy in age groups 16-61 and 16-40, indicate how limited those applications are in our group. Better results we obtained using ANNs, but higher homogeneity of our group (young women) and differences in the devices (single- or multi-frequency BIA) should be noted. Moreover in our study, we used a few raw BIA variables (not only resistance value at 50 kHz). Due to the fact that BIA data are influenced by various factors, e.g., device, patient positions (standing, lying), measurement conditions and electrode type, the obtained formulas can be specific for our study conditions. The present study showed the possibility of Etoto, Endo and Meso somatotypes calculation in young women using ANN based on the BIA results. Our study shows a very good agreement between ANN-based formulas and the Heath-Carter method. The proposed ANNs: MLP 4-4-1 (Endo and Meso) accurately describe the somatotypes (R \(\in\) (0.83-0.87), RMSE \(\in\) (0.66-0.76), \(\chi^{2}\)\(\in\) (0.66-0.87).