## 5 Model Verification and Comparative Analysis
### Qualitative and Quantitative Comparison  
We have compared the capabilities of SCM, GNW, AR, PSY, and DIKWP in handling uncertainty issues from the perspectives of data, information, knowledge, wisdom, and purpose. The comparison table is as follows (Table 11):  
To evaluate the capabilities of the five methods, we take the diagnostic difference between \(D_{A}\) and \(D_{C}\) in Case 2 as an example and compare these methods from both qualitative and quantitative perspectives. For the qualitative comparison, we deconstruct the dialogue between \(D_{C}\) and the patient sentence by sentence, comparing them based on three qualitative indicators: interpretability, dependency, and depth of analysis. Three sub-dialogues were selected for comparison, as follows:  
_[PERSON]'s Question: Doctor, I have been feeling tired recently and often experience low back pain. [PERSON]'s Question:Does your low back pain have a specific location? Is the pain associated with activity? Have you experienced any fever or weight loss? [PERSON]'s Answer:The lower back pain is not localized. It worsens with activity. I have not experienced any fever, and my weight appears stable._  
The detailed comparison is presented in Table 12. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\mathbf{U_{CS}}\) & \(\mathbf{U_{CP}}\) & \(\mathbf{U_{PR}}\) \\ \hline DAT & \(\{\textit{urine}\,\textit{routine},\textit{urineprotein}\}_{\}_{\}_{\}_{\}_{\}_{\}_{\} \)\(\times\) & \(\times\) \\  & \(\{\textit{CT}\,\textit{scan}\}_{\}_{\}_{\}_{\}_{\}{\}\) & & \\ INF & \(\{\textit{understand},\textit{renal}\}_{\}_{\}_{\}_{\}_{\}_{\}{\}_{\}\) & \(\times\) & \(\times\) \\  & \(\{\textit{look}\,\textit{for}\}_{\}_{\}_{\}{\}_{\}\) & \(\{\textit{fanny}\,\land\,\ldots\,\rightarrow\textit{renal}\}_{\}_{\}{\}_{\}{\}_{\}{\}_{\}{\} \textit{KNC_{A}}\) & \(\{\textit{fanny}\,\land\,\ldots\,\rightarrow\textit{renal}\}_{\}{\}_{\}{\}_{\}{\} \textit{KNC_{A}}\) \\ KNG & \(\times\) & AND & \(\mapsto\) \\  & \(\{\textit{ferror}\,\land\,\ldots\,\rightarrow\textit{stones}\}_{\}{\}_{\}{\}_{\}{\} \textit{KNC_{C}}\) & \(\{\textit{ferror}\,\land\,\ldots\,\rightarrow\textit{stones}\}_{\}{\}_{\}{\}{\} \textit{KNC_{C}}\) \\ WIS & \(\times\) & \(\times\) & \(\times\) \\ PUP & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table} 
Table 10: DIKWP treatment of PSY under uncertainty. We summarize the detailed content and compare the methods using three levels: \"High\", \"Medium\", and \"Low\" (as in Table 13). Due to the inherent subjectivity in qualitative comparisons, we conduct a more detailed quantitative comparison to better illustrate the advantages of the DIKWP model in aspects such as interpretability. In Case 2, where \(D_{A}\) and \(D_{C}\) prescribe different tests, we map the text content into the DIKWP graph. We compare the coverage of text processing by the five methods sentence by sentence (as shown in Figure 12). To process DIKWP uncertainty issues, we compare--sentence by sentence--the capabilities of these five methods in addressing the issue (as shown in Figure 13), taking the following as an example:  
\[ \begin{split} CoverageDAT_{SCM}=\frac{NumDAT_{SCM}}{NumDAT}\\ CoverageU_{SCM}=\frac{NumU_{SCM}}{NumU}\\ NumU_{SCM}=\sum_{i}NumU_{i\_SCM},i=DAT,INF,KNG,WIS,PUP\\ NumU_{PR\_SCM}=\sum_{j}Numj_{PR},j=DAT,INF,KNG,WIS,PUP\\ NumU_{CS\_SCM}=\sum_{m}Num_{CS},m=DAT,INF,KNG,WIS,PUP\\ NumU_{CP\_SCM}=\sum_{n}Num_{CP},n=DAT,INF,KNG,WIS,PUP\end{split}  \tag{37}\]  
Here, \(CoverageDAT_{SCM}\) represents the coverage rate of data in the text processed by the SCM method, and the same applies to other types. \(CoverageU_{SCM}\) stands for the total coverage rate of uncertainty, \(NumU_{SCM}\) represents the number of uncertainty issues, and \begin{table}
\begin{tabular}{c c c c c c} \hline  & **PSY** & **SCM** & **GNW** & **AR** & **DIKWP** \\ \hline Interpretability & Medium & Low & Medium & Medium & High \\ Dependence & Medium & High & Medium & High & Medium \\ Depth of analysis & High & Low & High & Medium & High \\ \hline \end{tabular}
\end{table}