## 3 Results  
Table 4 presents the performance measure of each trial. This table shows the F1-score for each class and the accuracy. In addition, the mean of 10 trials and the standard deviation (SD) between the trials are also shown in Table 4. According to the results in this table, our model obtained 93.06% mean accuracy. When class-based results are examined, it can be seen that all classes except \"Obesity Type II\" were well detected. Another aim of this study was to measure the effects of the features used in the dataset to determine the obesity level. In the next phase of the study, feature ranking approaches were applied to our dataset to achieve this aim. For this purpose, the SelectKBest function from the feature_selection package of the sklearn library was used in Python software [42] with the trainForVal and testForVal datasets. The scores of the features, calculated by score functions, are shown in Table 5. In this table, a low score indicates that the effect of the feature is higher in determining the class. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Trial**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**F1-Score**} \\  & & & **“Underweight”** & & & **Weight”** & & **Level II”** & **I”** \\ \hline
1 & 92.74\% & 88.89\% & 95.36\% & 96.29\% & 100.0\% & 100.0\% & 78.57\% & 90.90\% \\ \hline
2 & 93.55\% & 88.89\% & 96.00\% & 100.0\% & 100.0\% & 0\% & 80.00\% & 90.00\% \\ \hline
3 & 92.74\% & 88.89\% & 94.74\% & 100.0\% & 100.0\% & 100.0\% & 74.0\% & 95.24\% \\ \hline
4 & 96.77\% & 94.12\% & 97.99\% & 100.0\% & 100.0\% & 100.0\% & 90.32\% & 95.24\% \\ \hline
5 & 95.97\% & 94.12\% & 97.33\% & 100.0\% & 100.0\% & 100.0\% & 87.50\% & 94.74\% \\ \hline
6 & 92.74\% & 88.89\% & 95.36\% & 96.29\% & 100.0\% & 100.0\% & 78.57\% & 90.90\% \\ \hline
7 & 94.35\% & 94.12\% & 96.69\% & 100.0\% & 100.0\% & 100.0\% & 80.00\% & 90.00\% \\ \hline
8 & 91.13\% & 88.89\% & 93.51\% & 100.0\% & 100.0\% & 100.0\% & 66.67\% & 94.74\% \\ \hline
9 & 87.90\% & 84.21\% & 93.33\% & 88.00\% & 100.0\% & 0\% & 66.67\% & 80.00\% \\ \hline
10 & 92.74\% & 80.00\% & 95.89\% & 96.30\% & 100.0\% & 100.0\% & 87.50\% & 85.71\% \\ \hline
**mean** & **93.06\%** & **89.10\%** & **95.62\%** & **97.68\%** & **100\%** & **80\%** & **78.98\%** & **90.74\%** \\ \hline
**SD** & **2.34** & **4.27** & **1.43** & **3.62** & **0** & **40** & **7.76** & **4.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation criteria for obesity prediction of the trained neural network model for each trial. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Feature Name** & **Chi-Square** & **F-Classify** & **Mutual Information Classification** \\ \hline Gender & 6.01 & 2.00 & 0.048 \\ \hline Age & 59.91 & 7.13 & 0.023 \\ \hline Height & 0.06 & 2.99 & 0.001 \\ \hline Weight & 745.97 & 113.3 & 0.529 \\ \hline History & 7.94 & 4.64 & 0.132 \\ \hline FAVC & 1.68 & 1.25 & 0.007 \\ \hline FCVC & 0.60 & 1.32 & 0.022 \\ \hline NCP & 3.68 & 1.75 & 0.039 \\ \hline CAEC & 4.73 & 2.18 & 0.099 \\ \hline Smoke & 18.01 & 1.85 & 0.007 \\ \hline CHO & 4.47 & 1.97 & 0.014 \\ \hline SCC & 8.34 & 2.86 & 0.014 \\ \hline FAF & 3.25 & 0.50 & 0.006 \\ \hline TUE & 4.15 & 0.98 & 0.026 \\ \hline CALC & 0.86 & 0.95 & 0.011 \\ \hline MTRANS & 8.65 & 1.98 & 0.028 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Results of feature importance scores for feature selection methods. A bar chart of scores, shown in Figure 2, was drawn to show the effect of attributes. Since there is an inverse relationship between the score and the class determination effect, the y-axis of these bar charts represents the \(\frac{1}{score}\). Figure 2: Bar chart of feature class determination scores. Each chart shows the importance of features in the data in the corresponding feature selection algorithm.