# Red Fox Optimizer with Data-Science-Enabled Microarray Gene Expression Classification Model
## 4 Experimental Validation
### Resulting Analysis of RFODL-MGEC Technique on Ovarian Cancer Dataset  
Figure 9 illustrates a set of confusion matrices generated by the RFODL-MGEC algorithm on the test ovarian cancer dataset. For the entire dataset, the RFODL-MGEC technique categorized 159 images as ovarian and 87 images as normal. With 70% of the training dataset, the RFODL-MGEC algorithm categorized 102 images as ovarian and 69 images as normal. For 30% of the testing dataset, the RFODL-MGEC technique categorized 57 images as ovarian and 18 images as normal.  
Table 4 shows a brief classification performance report on the RFODL-MGEC technique with the ovarian cancer dataset. The experimental results indicated that the RFODL-MGEC technique demonstrated effective results on the test dataset. For instance, with the entire dataset, the RFODL-MGEC system obtained an average \(accu_{y}\), \(prec_{n}\), \(rec_{t}\), and \(F_{score}\) of 97.23%, 97.11%, 96.88%, and 96.99%, respectively. With 70% of the training dataset, the RFODL-MGEC algorithm obtained an average \(accu_{y}\), \(prec_{n}\), \(rec_{t}\), and \(F_{score}\) of 96.61%, 96.49%, 96.49%, and 96.49%, respectively. Eventually, with 30% of the testing dataset, the RFODL-MGEC algorithm obtained an average \(accu_{y}\), \(prec_{n}\), \(rec_{t}\), and \(F_{score}\) of 98.68%, 99.14%, 97.37%, and 98.21%, respectively.  
Figure 8: Loss analysis of RFODL-MGEC technique on colon tumor dataset.  
Figure 9: Confusion matrices of RFODL-MGEC technique on ovarian cancer dataset. (**a**) Entire dataset, (**b**) 70% of training dataset, and (**c**) 30% of testing dataset.  
Figure 10 illustrates the training and validation accuracy inspection of the RFODL-MGEC algorithm with the ovarian cancer dataset. The figure conveys that the RFODL-MGEC technique offered maximum training/validation accuracy for the classification process.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Ovarian Cancer Dataset** & & & & \\ \hline
**Class Labels** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline
**Entire Dataset** & & & & \\ \hline Tumor & 97.23 & 97.55 & 98.15 & 97.85 \\ \hline Normal & 97.23 & 96.67 & 95.6 & 96.13 \\ \hline
**Average** & **97.23** & **97.11** & **96.88** & **96.99** \\ \hline
**Training (70\%)** & & & & \\ \hline Tumor & 96.61 & 97.14 & 97.14 & 97.14 \\ \hline Normal & 96.61 & 95.83 & 95.83 & 95.83 \\ \hline
**Average** & **96.61** & **96.49** & **96.49** & **96.49** \\ \hline
**Testing (30\%)** & & & & \\ \hline Tumor & 98.68 & 98.28 & 100.00 & 99.13 \\ \hline Normal & 98.68 & 100.00 & 94.74 & 97.30 \\ \hline
**Average** & **98.68** & **99.14** & **97.37** & **98.21** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Resulting analysis of RFODL-MGEC technique with various measures on ovarian cancer dataset.  
Figure 10: Accuracy analysis of RFODL-MGEC technique with ovarian cancer dataset.  
Figure 11 exemplifies the training and validation loss inspection of the RFODL-MGEC technique on the ovarian cancer dataset. The figure shows that the RFODL-MGEC system offered reduced training/accuracy loss for the classification process of the test data.