## 6 Experimental Results and Analysis
### Analysis of Experimental Results
#### 6.3.4 Impact of Hybrid Feature Selection Algorithms on Model Performance  
In order to verify the effectiveness of hybrid feature selection, we set up a control group and an experimental group for comparison. The data in the control group are only screened by the Pearson correlation coefficient in feature selection, followed by prediction. The data in the experimental group are screened by the hybrid feature selection method proposed in this paper, and then the prediction of breast cancer is carried out. The comparison results are shown in Table 7.  
According to Table 7 and Figure 5, it can be seen that, compared with the control group, the accuracy, recall, and Fl-score of the experimental group of the BGD-LR model improved by 2.63%, 3.41%, and 1.76%, respectively. For the BDP-LR model, when the privacy budget \(\varepsilon\) added in each iteration is 0.2, the four main evaluation indicators of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model** & **Privacy** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{Control group} & BGD-LR & 0 & 0.9649 & 1.0000 & 0.9545 & 0.9767 \\ \cline{2-6}  & & 0.2 & 0.8936 & 0.9840 & 0.8765 & 0.9256 \\ \cline{2-6}  & & 0.4 & 0.9249 & 0.9903 & 0.9117 & 0.9490 \\ \cline{2-6}  & & 0.6 & 0.9454 & 0.9928 & 0.9361 & 0.9634 \\ \cline{2-6}  & & 0.8 & 0.9563 & 0.9949 & 0.9483 & 0.9709 \\ \cline{2-6}  & & 1 & 0.9566 & 0.9954 & 0.9482 & 0.9711 \\ \hline \multirow{6}{*}{Experimental group} & BGD-LR & **0** & **0.9912** & **1.0000** & **0.9886** & **0.9943** \\ \cline{2-6}  & & **0.2** & **0.9170** & **0.9849** & **0.9065** & **0.9431** \\ \cline{1-1} \cline{2-6}  & & **0.4** & **0.9561** & **0.9933** & **0.9495** & **0.9706** \\ \cline{1-1} \cline{2-6}  & & **0.6** & **0.9629** & **0.9959** & **0.9559** & **0.9753** \\ \cline{1-1} \cline{2-6}  & & **0.8** & **0.9721** & **0.9975** & **0.9664** & **0.9816** \\ \cline{1-1} \cline{2-6}  & & **1** & **0.9777** & **0.9981** & **0.9731** & **0.9853** \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of results before and after hybrid feature selection.  
Figure 4: Loss function in the BGD-LR model.  
model increase by 2.34%, 0.09%, 3.00%, and 1.75%, respectively. When the privacy budget \(\varepsilon\) is 0.4, the four main evaluation indicators of the model improve by 3.12%, 0.3%, 3.78%, and 2.16%, respectively. When the privacy budget \(\varepsilon\) is 0.6, the indicators of the model increase by 1.75%, 0.31%, 1.98%, and 1.19%, respectively. When the privacy budget \(\varepsilon\) is 0.8, the four main evaluation indicators improve by 1.58%, 0.26%, 1.81%, and 1.07%, respectively. And when the privacy budget \(\varepsilon\) is 1, the four main evaluation indicators increase by 2.11%, 0.27%, 2.49%, and 1.42%, respectively. Obviously, after the breast cancer data are processed by hybrid feature selection, the model classification results of the experimental group are all better than those of the control group, therefore, hybrid feature selection can effectively improve the classification performance of the model.  
After comparative analysis, it can be seen that the optimal accuracy, precision, recall, and \(F1\)-score of the BGD-LR model are 0.9912, 1, 0.9886, and 0.9943, respectively. When the added privacy budget \(\varepsilon\) is 1, the BDP-LR model has the best combined classification  
Figure 5: Comparison of experimental results for control and experimental groups: (**a**) accuracy; (**b**) precision; (**c**) recall; (**d**) \(F1\)-score.  
results, and its accuracy, precision, recall, and Fl-score are 0.9777, 0.9981, 0.9731, and 0.9853, respectively.