## 4 Discussion
The reported sensitivity, specificity, precision, accuracy, and F1 scores were 92.7, 92.8, 94.8, 95.7, and 95.2, respectively, as illustrated in Figure 12. These scores indicate the high potential of the introduced model in the early detection of RA biomarkers. The objective of EX04 was to study the impact of the used samples' preparation kits on the accuracy of the detection of the disease's biomarkers (Model_I classified the samples into cases, controls, and synthetic samples). Thus, Model_I was experimented on using Dataset_I twice; first with the samples prepared by NEBNEXT separately, and second with the samples prepared by NEXTFLEX, and the detection accuracy scores were recorded accordingly. As Figure 13 shows, the obtained results from Ex04 imply that the samples that are prepared using _NEXTFLEX_ report higher accuracy scores of RA biomarker detection than the samples prepared by NEBNEXT. In Ex05, the objective was to test Model_I on sensitive data. The synthetic data prepared by NEBNEXT and NEXTFLEX libraries were classified into A, B, C, D, and E. The reported accuracy scores in Ex05 were relatively low. However, the classification accuracy scores reported by samples prepared by NEXTFLEX were relatively higher, which supports the results of Ex04. These results can be explained since the classified data in this experiment are synthetic, and the biomarkers being classified throughout classes A, B, C, D, and E are not relatively significant. Figure 14 illustrates the reported scores when classifying the prepared data by NEBNEXT and NEXTFlex kits. Figure 15 consolidates the obtained accuracy scores for Dataset_I with _Model_I_, indicating that the highest scores obtained were in EX04 using the NEXTflex preparation kit. Consequently, the results of EX04 and EX05 denote that the transcriptomic samples prepared by NEXTFLEX obtained higher biomarker detection accuracy when used with Model_I. The obtained results for Ex06 (Model_I with Dataset_II) and Ex07 (Model_0 with Dataset_IV) confirm the robustness of detecting MS disease biomarkers with low and higher sample datasets. The results are also aligned with the accuracy scores in our previous work. Figure 16 summarizes the results of both EX06 and EX07. On the other hand, the experiments applied on the deep learning model (Model_II) using Dataset_VI reported a relatively lower disease detection accuracy. This is explained by the fact that the number of the studied samples (239 sequence files) was inconsiderable compared to the number of the obtained features in the dataset (hundreds of thousands). Thus, the sensitivity, specificity, precision, accuracy, and F1 scores of EX08 were 66.4, 66.4, 66.1, 68, and 67.8, respectively, as shown in Figure 16.  
To increase the number of the studied samples in the dataset, in Ex09, Dataset_VI was fragmented using the fragmentation techniques of Model_I. Fragmentation sizes were \(f\) = (0.1, 0.2, and 0.3). Noting this, the fragmentation size \(f\) = 0.2 was applied based on the results of EX01, and, accordingly, one point above and one point below the value of 0.2 were obtained. No further fragmentation sizes were tested since no increase in the detection accuracy was reported with the values 0.1 or 0.3. After fragmentation, each fragment was considered for one miRNA sequence in the experimented dataset (the first layer of fragmentation in Model_I before clustering was applied). This increase in the dataset size elevated the model accuracy score to 0.72. This increase in the dataset size is still not comparable to the number of the extracted features. Hence, the obtained results denote that the highest accuracy score of 0.72 reported with Model_II was with a fragmentation size of 0.2, implying that the resulting sequences of fragmentation size (0.2) have distinctive features, while reducing the fragmentation size to 0.1 significantly reduces the number of indicative features and consequently impacts the model's prediction accuracy.