## 4 Discussion  
In this study, we developed a neural network-based classification model for the prediction of obesity levels based on physical activity levels and eating habits. The F-Classify, chi-square, and mutual information classification algorithms were used to identify risk factors associated with obesity. The hyperparameters of the models were optimized using \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Hyperparameters**} & \multirow{2}{*}{**Chi-Square**} & \multirow{2}{*}{**F-Classify**} & **Mutual Information** \\  & & & **Classification** \\ \hline n\_unit\_dense & 32 & 65 & 120 \\ \hline lr & 0.085 & 0.0086 & 0.024 \\ \hline epoch & 758 & 1200 & 967 \\ \hline batch & 16 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimum values of hyperparameters of the neural network model that are trained using features selected by feature selection algorithms. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Feature Selection Method** & **Accuracy** & **F1-Score** & **SD Accuracy** & **SD F1-Score** & **Sensitivity** & **Specificity** & **Brier Score** \\ \hline Original Model & 93.06\% & 92.79\% & 2.34 & 3.29 & 93.08\% & 93.60\% & 0.094 \\ Chi-Square & 89.04\% & 89.36\% & 1.57 & 1.63 & 89.03\% & 90.60\% & 0.147 \\ F-Classify & 90.32\% & 89.74\% & 1.78 & 1.72 & 90.27\% & 89.84\% & 0.122 \\ Mutual Information Classification & 86.52\% & 86.56\% & 2.44 & 2.35 & 86.55\% & 87.40\% & 0.194 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance evaluation criteria for trained neural network and comparison of different feature selection methods. the Bayesian optimization technique, which is faster and more effective than traditional techniques. The results show that for the purpose of estimating obesity levels, results close to those of the model in which all features were used can be obtained using fewer features. The best features were selected using the F-Classify score function, and physical activity and dietary habits were important for obesity prediction. The model trained with the features selected using chi-square was the most robust. Furthermore, according to the Brier score results, the calibration of the model trained after F-Classify was the best among the feature selection approaches. The results were compared with the two most recent established methods in the literature for the purpose of estimating the level of obesity. The first method was developed by [PERSON] using deep learning, and 82.00% accuracy was obtained [1]. In the current study, the trained neural network model was more successful in estimating obesity levels. Even the lowest-performing model, using features selected by the reciprocal information classification, achieved statistically better accuracy than a two-tailed Z-test [43], with \(p\) values less than 0.1. In another study in the literature, several decision tree algorithms were used by [PERSON] et al. [44], and 97.8% accuracy was obtained in the estimation of obesity level using the J48 algorithm. The J48 model achieved 0% accuracy in classifying the \"Obesity Type III\" class, although the authors achieved better accuracy than the models trained in the current study. Therefore, the model developed in the current study can be considered to be more robust than this model. In addition, most of the studies in the relevant literature have focused on identifying risk factors that cause obesity. In one study, the authors defined the overall pattern of pediatric obesity development and the onset of early childhood obesity. However, they developed the XGBoost model to predict whether individuals had early obesity. With the XGBoost model, the accuracy was 30.90%, the F1-score was 44.60%, the accuracy was 66.14%, and the specificity was 63.27% [45]. Another study was conducted with a total of 7162 participants, and 11 different methods were applied to predict obesity.