## 4 Results and Discussions
### Discussion  
Detailed analysis of models based on machine learning modeling approaches is described in the previous section. All machine learning models were capable of predicting BFP using the considered primary dataset; however, the proposed SVR-EANN outperformed other models by achieving superior results for all evaluation metrics used in this study. In this section, the results of real data analytics are analogized with the literature. There were several studies in the literature in the context of BFP prediction using ML algorithms. Apart from basic information and anthropometric measurements such as gender, age, BMI, height, weight, and waist circumference, single-stage intelligent predictors such as ANN and/or SVR were used to forecast BFP [1,8,9]. System performance in these studies varied between 44% and 80.43%. However, these studies included random \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & \(\mathbf{R^{2}}\) & **RMSE** & **RRMSE (\%)** \\ \hline FFNN & 0.8573 & 0.0622 & 19.6919 \\ \hline EANN & 0.8928 & 0.0464 & 13.5565 \\ \hline SVR-EANN & 0.9911 & 0.0125 & 3.1513 \\ \hline SVR & 0.9682 & 0.0245 & 7.6956 \\ \hline DT & 0.9699 & 0.0216 & 6.9877 \\ \hline RF & 0.9747 & 0.0198 & 6.3225 \\ \hline XGBOST & 0.9807 & 0.0178 & 5.9125 \\ \hline GRADBOOST & 0.9802 & 0.0182 & 5.9949 \\ \hline LR & 0.9185 & 0.0396 & 11.4356 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: BFP prediction results. Figure 6: Scatter plots of the testing stage for predicted BFP: (**a**) SVR-EANN, (**b**) EANN, (**c**) FFNN. selection of parameters and, nevertheless, could identify the most significant parameters to reach the best predictor variables. On the other hand, studies with hybrid machine learning in BFP prediction were proposed. These models contain a feature selection model that varies between the intelligence-based or statistic-based models to identify the most explanatory variables for the intelligent forecasting algorithm phase. A model based on multiple regression (MR) and multivariate adaptive regression splines (MARSs) was employed in [11] to choose the best variables in the explanatory phase which resulted in two sets with five and six variables among 13 anthropometric parameters for the MR and MARSs, respectively. The most relevant variables included age, height, weight, abdomen 2 circumference (waist circumference), forearm circumference, waist circumference, thigh circumference, and neck circumference. In contrast, when ANN and SVR were utilized for BFP prediction, the MR-SVR achieved the best performance with an RMSE of 4.6427. Hybrid models proposed by [PERSON] [12] employed [PERSON]'s correlation coefficients and principal component analysis computations for the feature selection phase to obtain the most predictive feature group from 13 different subgroups of anthropometric measurements. The intelligent hybrid models were constructed from the MLFFNN, DT, and SVM algorithms. The most significant result was achieved through the hybrid model DT-SVM with one anthropometric measurement named abdomen 2 circumference (waist circumference) and an RMSE of 0.482. However, both hybrid studies [11; 12] considered the dataset collected by [PERSON] [10] from 252 male participants. Therefore, the gender feature was not discussed. In addition, both studies could not achieve higher results as in this study in terms of prediction rates, even though the dataset size was considerably small. According to studies in the literature, certain parameters are effective in the estimation of BFP. Gender is the most evident factor [42; 43; 44]. A common equation for men and women has been derived in several investigations [8; 45]. Gender information is included in the dataset used in this study. In addition to gender, benchmark studies on ML and BFP prediction with single or hybrid intelligent algorithms indicated the relative importance of other features such as BMI [1] and waist circumference [8; 10; 11].