## 3 Results and Discussion
### The Classification of the Matrix Architecture for the Different Orders  
In order to perform the classification, the techniques employed in the previous study are applied here too [20]. To establish the variation interval for classification, the variance values for the third- and the fifth-order PMLD matrices are utilized from Tables 2 and 3. The variance values are fitted to the Gaussian distribution as shown in Figure 2. The variance values for the healthy candidates are denoted by blue circles along the \(x\)-axis and the variance values for unhealthy individuals are represented by red circles as depicted in Figure 2. Once the distributions are in place, the normal distribution of the dataset (the variance values) is validated by performing the Anderson-Darling test. The Anderson-Darling test is chosen to validate the normal distribution of the variance values because of its sensitivity to deviations in the tails of the distribution, which is crucial for our dataset and is also utilized in [20]. Also, the 95% confidence intervals for the mean and variance values of both healthy and unhealthy individuals are used. The same confidence intervals are also used in [17]. Upon confirming the normal distribution, we proceed with applying the one sigma rule to the distributed dataset for the third- and the fifth- order PMLD matrices for the generation of the variation interval. To do so, the one sigma rule is applied upon the distributed dataset where the left boundary is defined as mean minus sigma \(\left(u_{\text{h}}^{3}-\sigma_{\text{h}}^{3}\right)\) and the right boundary is established as mean plus sigma \(\left(u_{\text{u}}^{3}+\sigma_{\text{u}}^{3}\right)\) for the third-order PMLD matrix as shown in Figure 2a. Similarly, the same statistical technique is adopted to apply the one sigma rule for the fifth-order PMLD matrix where the left boundary is defined as mean minus sigma \(\left(u_{\text{h}}^{5}-\sigma_{\text{h}}^{5}\right)\) and the right boundary is defined as \(\left(u_{\text{u}}^{5}+\sigma_{\text{u}}^{5}\right)\). The variation interval is built for classification purposes and is visualized by the double-headed arrow as depicted in Figure 2b. This technique assures both clarity and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**List of Unhealthy Candidates**} & \multicolumn{2}{c}{**2 nd-Order Matrix**} & \multicolumn{2}{c}{**3 rd-Order Matrix**} & \multicolumn{2}{c}{**4 th-Order Matrix**} & \multicolumn{2}{c}{**5 th-Order Matrix**} \\ \cline{2-7}  & **JT, QRS** & **JT, RR** & **JT, QRS, RR** & **JT, QRS, RR, DP** & **JT, QRS, RR, AP, DP** \\ \hline U\({}_{1}\) & 0.0005 & 0.0001 & 0.0006 & 0.0031 & 0.0034 \\ \hline U\({}_{2}\) & 0.0002 & 0.0001 & 0.0004 & 0.0004 & 0.0004 \\ \hline U\({}_{3}\) & 0.0073 & 0.0023 & 0.0082 & 0.0229 & 0.0133 \\ \hline U\({}_{4}\) & 0.0049 & 0.0012 & 0.0064 & 0.0039 & 0.0065 \\ \hline U\({}_{5}\) & 0.0014 & 0.0003 & 0.0030 & 0.0100 & 0.0117 \\ \hline U\({}_{6}\) & 0.0007 & 0.0004 & 0.0014 & 0.0036 & 0.0039 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The values for the variance of the norm of the PMLD matrix (from the second to the fifth orders) for the unhealthy cohort. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**List of Healthy Candidates**} & \multicolumn{2}{c}{**2 nd-Order Matrix**} & \multicolumn{2}{c}{**3 rd-Order Matrix**} & \multicolumn{2}{c}{**4 th-Order Matrix**} & \multicolumn{2}{c}{**5 th-Order Matrix**} \\ \cline{2-6}  & **JT, QRS** & **JT, RR** & **JT, QRS, RR** & **JT, QRS, RR, DP** & **JT, QRS, RR, AP, DP** \\ \hline H\({}_{1}\) & 0.0018 & 0.0004 & 0.0012 & 0.0069 & 0.0096 \\ \hline H\({}_{2}\) & 0.0015 & 0.0002 & 0.0029 & 0.0029 & 0.0074 \\ \hline H\({}_{3}\) & 0.0013 & 0.0009 & 0.0023 & 0.0030 & 0.0150 \\ \hline H\({}_{4}\) & 0.  
precision in establishing classification boundaries, allowing for accurate categorization for individuals based on the variance values. To enhance the classification accuracy of test candidates, a machine-trained model was developed.