## 5 Results and Discussion
The features for model 3 are selected using Random Forest. 2 \(\upmu\) is used as feature importance threshold and that produces 1656 features. Table 4 shows that the best model when using Chi-Square as a feature selection method is model 1 with a tuning accuracy of 96.69% at a feature importance threshold equal to 0.5 \(\upmu\) which produces 7137 features. From the above results, it is clear that the best model is the model that resulted from using the Lasso as a features selection method at a feature importance threshold equal to 4 \(\upmu\). The accuracy obtained from evaluating the best model on the test set is 96.16% with test std: (+/\(-\)0.40%). The classification report of the best model for each cancer type and the normal cases is depicted in Table 4. Table 4 shows that the f1-score provides the mean of precision and recall. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model No.**} & **CV Tuning** & \multicolumn{3}{c}{**Threshold**} \\  & **Average** & **Tuning Std** & **(Feature** & **No. of Features** \\  & **Accuracy** & & **Coefficient)** & \\ \hline
**1** & 96.69\% & **(+/\(-\)0.30\%)** & \(\frac{1}{2}\upmu\) & **7137** \\
2 & 96.52\% & (+/\(-\)0.30\%) & \(\mu\) & 4432 \\
3 & 96.29\% & (+/\(-\)0.41\%) & \(2\upmu\) & 2220 \\
4 & 95.06\% & (+/\(-\)0.34\%) & \(4\upmu\) & 688 \\
5 & 85.59\% & (+/\(-\)1.08\%) & \(8\upmu\) & 64 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Models accuracies and the number of features when using Chi-Square with the different thresholds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model No.**} & **CV Tuning** & \multicolumn{3}{c}{**Threshold**} \\  & **Average** & **Tuning Std** & **(Feature** & **No. of Features** \\  & **Accuracy** & & **Coefficient)** & \\ \hline
1 & 96.76\% & (+/\(-\)0.41\%) & \(\frac{1}{2}\upmu\) & 7535 \\
2 & 97.03\% & (+/\(-\)0.37\%) & \(\upmu\) & 4865 \\
3 & 96.98\% & (+/\(-\)0.43\%) & \(2\upmu\) & 2356 \\
**4** & **97.11\%** & **(+/\(-\)0.42\%)** & \(4\upmu\) & **597** \\
5 & 95.18\% & (+/\(-\)0.80\%) & \(8\upmu\) & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Models accuracies and the number of features when using Lasso with the different thresholds. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model No.**} & **CV Tuning** & \multicolumn{3}{c}{**Threshold**} \\  & **Average** & **Tuning Std** & **(Feature** & **No. of Features** \\  & **Accuracy** & & **Coefficient)** & \\ \hline
1 & 96.83\% & (+/\(-\)0.25\%) & \(\frac{1}{2}\upmu\) & 5166 \\
2 & 96.93\% & (+/\(-\)0.08\%) & \(\upmu\) & 2896 \\
**3** & **96.94\%** & **(+/\(-\)0.28\%)** & \(2\upmu\) & **1656** \\
4 & 96.92\% & (+/\(-\)0.14\%) & \(4\upmu\) & 870 \\
5 & 96.59\% & (+/\(-\)0.15\%) & \(8\upmu\) & 321 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models accuracies and the number of features when using Random Forest with the different thresholds. The scores given to each class will show how well the classifier classified the data points within that class in relation to all other classes. Table 5 shows that the proposed model has very high identification ability on the classes BRCA, CESC, HNSC, LGG, PCPG, PRAD, SKCM, TGCT, THCA, and UCEC with F1-score ranging between 98% and 99%. In addition, the table shows that the proposed model performed very weak in classifying CHOL class, where only 35 samples from CHOL class were included in the modeling process. Figure 3 shows the proposed model Confusion matrix on 33 tumor types in addition to the normal samples. By examining the confusion matrix carefully, it is clear that the majority of errors are in the classification of READ, KICH, ESCA, and CHOL. For the READ (Rectum adenocarcinoma or rectal cancer) cancer type, 39% of the samples were misclassified as COAD (colon adenocarcinoma), while 12% of the samples of COAD were misclassified as READ. In the rectal cancer, the tissues of the rectum evolve into cancerous (malignant) cells.