## 4 Experimental Results
### Discussion  
In the plot scenarios depicted in Figure 7, the red dots represents the users (i.e., FBC patients) and the values on the y-axis indicate the SAC's FBC assessment scores, whereas on the x-axis is the doctor's FBC screening scores which we considered in this study as the fixed benchmark standard. In case 1(a), it was observed that the performance of the SAC agent was very poor. Herein, for example, the batch of users that was scored 10 by the doctors received between 4.1 to 5.9 scores from the chatbot. Therefore, we introduced an adjustable scaling coefficient \(\alpha\) in the algorithm in Equation (1) to address the low score outcome. Thereafter, in subsequent retraining of the SAC model, we experimentally varied the value of the \(\alpha\) and observed improved scoring abilities of the agent as shown in Figure 6(b),\(c\), respectively. In Figure 6(d), we adjusted the scaling value of \(\alpha\) until we arrived at 1.53, and then also, we slightly modified the values of the weight factors to \(f_{A}=0.63\), \(f_{B}=0.22\), and \(f_{C}=0.15\), upon which, after completion of the training iterations, we achieved up to 95.4% assessment scoring accuracy. In this instance, only a few variations was observed between the agent's score (\(S_{1}\)) and the FBC doctors' score (\(S_{2}\)). For example, the lower end user's FBC symptom that was scored 10 by the doctor was also likewise scored 9.2 by the chatbot, both of which are equally within the bounds of serious FBC ailment confirmation.  
In Figure 8, we utilized more statistical representation approaches to further analyze SAC's assessment scoring performance in comparison to the FBC assessment benchmark standard set by the FBC doctors. The FBC prescreening scores of SAC was compared against that of the doctors using the same group of patients. Since several distributed users can access and chat with SAC at the same time over the network, we subjected the SAC to multiple instances of textual dialogs using the same responses from the patients' Q&A data. In the SAC's testing process, a total number of 135 anonymized patients' Q&A responses to the doctors' FBC screening questions were used. In the graph plot of Figure 7(a), the blue plot represents the doctors' screening scores for each of the 135 patients, whereas the corresponding orange plot represents the SAC's FBC assessment scores of the same patients. Keen observation of the graph plot outcome in Figure 8 indicates a high correlation accuracy achieved by SAC in FBC assessment scoring versus the benchmark scores from the CGMH FBC specialist doctors.  
Figure 8: Graphical plot and Histogram showing accuracy comparison between SAC’s assessment scoring of FBC patients versus the doctors’ benchmark FBC screening scores.  
In addition, we also used the histogram in Figure 8b to show a cleaner comparative outcome of SAC's score accuracy versus the FBC doctors' scores using a random sample of 10 users. Here, the slight variations in both scores are clearly visible; however, irrespective of that, the histogram chart also shows that the SAC's scores meets up within the bounds of the doctors' score standard as described in Figure 7d.