# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 4 Experimental Validation
### Discussion  
A detailed comparative examination of the RFODL-MGEC model with recent approaches [15] for prostate cancer is provided in Table 5 and Figure 12. The experimental outcomes indicated that the FFSDL and ESADL models reached lower classification outcomes than other approaches. At the same time, the SVM and RF models accomplished slightly enhanced classification outcomes compared with the FFSDL and ESADL models. Along with that, the ABC-SVM and PSO-SVM models accomplished closer classification performances, with an \(accu_{y}\) of 96.06% and 93.71%, respectively. The proposed RFODL-MGEC model resulted in maximum classification efficiency, with an \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 96.77%, 96.88%, and 96.88% respectively. A brief comparative examination of the RFODL-MGEC approach with recent approaches for colon tumors is given in Table 6 and Figure 13. The experimental outcomes indicated that the FFSDL and ESADL approaches reached lower classification outcomes than the other approaches. Likewise, the SVM and RF approaches accomplished somewhat enhanced classification outcomes compared with the FFSDL and ESADL approaches. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Colon Tumor** & & & \\ \hline
**Methods** & **Accuracy** & **Precision** & **Recall** \\ \hline SVM Model & 83.81 & 84.39 & 83.77 \\ \hline RF Model & 88.26 & 89.41 & 87.36 \\ \hline FFSDL & 88.26 & 89.48 & 87.99 \\ \hline ESADL & 88.97 & 88.77 & 89.11 \\ \hline ABC-SVM Model & 93.94 & 94.29 & 92.31 \\ \hline PSO-SVM Model & 93.80 & 94.90 & 93.37 \\ \hline RFODL-MGEC & 94.74 & 95.83 & 93.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative analysis of RFODL-MGEC technique with recent algorithms for colon tumor dataset. Figure 12: Comparative analysis of RFODL-MGEC technique with prostate cancer dataset. Along with that, the ABC-SVM and PSO-SVM models accomplished closer classification performances, with an \(accu_{y}\) of 93.94% and 93.80%, respectively. Finally, the RFODL-MGEC model resulted in higher classification efficiency with an \(accu_{y}\), \(prec_{n}\), and \(reca_{1}\) of 94.74%, 95.83%, and 93.75% respectively. A detailed comparative examination of the RFODL-MGEC algorithm with recent approaches for ovarian cancer is given in Table 7 and Figure 14. The experimental outcomes indicated that the FFSDL and ESADL methods reached lower classification outcomes than the other approaches. \begin{table}
\begin{tabular}{l l l l} \hline
**Ovarian Cancer** & & & \\ \hline
**Methods** & **Accuracy** & **Precision** & **Recall** \\ \hline SVM Model & 84.71 & 83.92 & 85.98 \\ \hline RF Model & 86.79 & 87.86 & 86.36 \\ \hline FFSDL & 86.56 & 87.82 & 85.58 \\ \hline ESADL & 89.23 & 89.84 & 88.74 \\ \hline ABC-SVM Model & 95.42 & 95.56 & 95.86 \\ \hline PSO-SVM Model & 95.81 & 96.01 & 96.49 \\ \hline RFODL-MGEC & 98.68 & 99.14 & 97.37 \\ \hline \end{tabular}
\end{table} 
Table 7: Comparative analysis of RFODL-MGEC technique with recent algorithms for ovarian cancer dataset. Figure 13: Comparative analysis of RFODL-MGEC technique with colon tumor dataset. The SVM and RF models accomplished some enhanced classification outcomes compared with the FFSDL and ESADL models. This was followed by the ABC-SVM and PSO-SVM techniques, which accomplished closer classification performances with an \(accu_{y}\) of 95.42% and 95.81%, respectively. Finally, the RFODL-MGEC methodology resulted in maximum classification efficiency, with an \(accu_{y}\), \(prec_{n}\), and \(reca_{l}\) of 98.68%, 99.11%, and 97.37%, respectively. Finally, a computation time (CT) examination of the RFODL-MGEC technique with recent models for the three distinct datasets is provided in Table 8. The experimental results indicated that the RFODL-MGEC technique showed a lower CT compared with the other methods. The proposed RFODL-MGEC technique required a lower CT of 1.231, 0.432, and 1.542 s with the test prostate cancer, colon tumor, and ovarian cancer datasets, respectively.