## 2 Materials and Methods  
### Datasets  
The aim of collecting primary data for this study was to develop a BFP prediction model capable of predicting BFP with high accuracy rates using the relevant anthropometric attributes and determine the most significant parameters that affect the prediction model. The dataset consisted of eight attributes (gender, age, height, weight, abdominal C, WHR (the ratio of waist circumference/hip circumference), BMI (weight/height\({}^{2}\)), and BFP).  
The BFP was estimated by a body composition analysis device named BIA ACCUNIQ BC380 positioned in the Department of Food and Nutrition of Baxshin Hospital, Iraq, under the supervision of specialized doctors. The abdominal C in this study represented the waist circumference which is a \"constant measure of abdominal obesity\" [20]. Data were collected under the supervision of expert staff, and special procedures were followed to estimate the metric parameters as well as the body composition of 2000 observations of 387 males and 1613 females. The ages were between 18 and 29--the parameters were selected according to the importance of anthropometric models in historical research on BFP estimation.  
The collected data consisted of a single observation for each person and did not contain time-series data to predict the future BFP by considering the person's recent anthropometric and laboratory measurements. Therefore, the study focused on the accurate prediction of the recent BFP of a person to minimize the time and costs to obtain BFP. The summary of the descriptive statistics of the data is provided in Table 1.  
Initially, the Pearson correlation matrix was created to show the linear dependencies between the attributes (variables) and observe the strength of the correlation [21]. It was observed that BFP had a higher correlation with the BMI followed by the abdominal C and weight with correlation coefficients of 0.8086, 0.7583, and 0.6277, respectively. Age, WHR, and height were found to be the variables with the lowest correlation with the BFP. The correlation matrix between the variables and the graphical representation is shown in Table 2 and Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Parameters** & **BMI** & **Abdominal C** & **Weight** & **Gender** & **Height** & **WHR** & **Age** & **BFP** \\  & **(kg/m\({}^{2}\))** & **(cm)** & **(kg)** & **Gender** & **(cm)** & **(cm)** & **Age** & **BFP** \\ \hline Mean & 26.218 & 86.6345 & 67.05 & 1.81 & 160.301 & 0.825 & 23.05 & 33.42 \\ \hline Standard & 10.33 & 18.031 & 21.761 & 0.40 & 9.4638 & 0.0554 & 3.43 & 11.83 \\ \hline Minimum & 12.9 & 60.7 & 15.40 & 1.00 & 63.00 & 0.66 & 18.00 & 3.00 \\ \hline Maximum & 237.3 & 182.7 & 183.70 & 2.00 & 199.8 & 2.1 & 29.00 & 87.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **BMI** & **Abdominal C** & **Weight** & **Gender** & **Height** & **WHR** & **Age** & **BFP** \\ \hline BMI & 1 & & & & & & & \\ \hline abdominal C & 0.9531 & 1 & & & & & & \\ \hline weight & 0.9279 & 0.9589 & 1 & & & & & \\ \hline gender & 0.0118 & \(-\)0.155 & \(-\)0.2183 & 1 & & & & \\ \hline height & \(-\)0.0036 & 0.1203 & 0.3288 & \(-\)0.6017 & 1 & & & \\ \hline WHR & 0.0047 & \(-\)0.0698 & \(-\)0.2683 & 0.4822 & \(-\)0.9358 & 1 & & \\ \hline age & 0.2479 & 0.2042 & 0.2216 & 0.0289 & \(-\)0.0059 & 0.0490 & 1 & \\ \hline BFP & 0.8086 & 0.7583 & 0.6277 & 0.4411 & \(-\)0.3979 & 0.3643 & 0.2097 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coefficients of correlation matrix between the variables.