## 3 Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Metrics** & **1/6** & **1/4** & **1/3** \\ \hline \multirow{2}{*}{Mean Absolute Error} & 0.2297 & 0.0882 & 0.0484 \\  & (0.14, 0.31) & (0.05, 0.12) & (0.03, 0.06) \\ \hline \multirow{2}{*}{Root Mean Squared Error} & 0.2827 & 0.1243 & 0.0551 \\  & (0.17, 0.38) & (0.07, 0.17) & (0.03, 0.0757) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance indicators of the regressor for assessing the degree of affinity between class P and classes S and V with confidence intervals. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metrics** & **S** & **SI** & **V** & **VI** \\ \hline \multirow{2}{*}{Precision} & 0.9882 & 0.9677 & 0.9492 & 0.9661 \\  & (0.98, 0.99) & (0.95, 0.97) & (0.93, 0.96) & (0.95, 0.97) \\ \hline \multirow{2}{*}{Sensitivity} & 0.9871 & 0.9423 & 0.9655 & 0.95 \\  & (0.97, 0.99) & (0.92, 0.96) & (0.95, 0.97) & (0.93, 0.96) \\ \hline \multirow{2}{*}{Specificity} & 0.9531 & 0.9769 & 0.954 & 0.9652 \\  & (0.94, 0.96) & (0.96, 0.98) & (0.94, 0.96) & (0.95, 0.97) \\ \hline \multirow{2}{*}{Specificity} & 0.9895 & 0.9638 & 0.9573 & 0.958 \\  & (0.98, 0.99) & (0.9522, 0.9754) & (0.94, 0.96) & (0.94, 0.97) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance indicators of the joint work of the global classifier and binary classifiers. Statistical significance tests comparing the original and hybrid encoder models showed that the hybrid model was superior to the original model for classes P, S, SI, V, and VI. This was evident from the results of the McNemar test, which had a high degree of confidence (\(p<0.001\)) and medium to huge Cohen's h values (h \(>0.6\)). Because the N classification results were initially high, there were no significant improvements in its detection. A remarkable improvement in classification accuracy across all stages of AMD was noted. The most advanced stages, including stages IV (SI), V, and VI, now show an accuracy exceeding 96%. The classification of geographic atrophy (GA) at stage I has been accomplished with perfect accuracy, reaching a remarkable 100%. There has been a significant reduction in misclassifications between adjacent stages, such as N \(\rightarrow\) S \(\rightarrow\) P. The latter stages of AMD, SI, V, and VI have been more accurately differentiated, with a minimum number of cross-classification errors. The model exhibits a consistent performance across all stages of AMD, addressing the previously identified imbalance where the late-stage classes performed poorly, ensuring a more equitable assessment. Despite the improvements, some bugs persist, which require attention for further stages of CDSS development:  
1. A total of 5% of early-stage AMD cases were mistakenly classified as healthy retina, which is important to pay attention to for the possibility of early intervention. Meanwhile, 6% of early-stage AMD was misdiagnosed as geographical atrophy. 2. A total of 4% of intermediate-stage AMD cases were mistakenly attributed to the early stage, and 2% of early stage AMD cases were mistakenly attributed to the middle stage. These errors, although less critical, can lead to minor delays. Figure 8: Error matrix of hybrid classifier. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Test** & **N** & **P** & **S** & **SI** & **V** & **VI** \\ \hline [PERSON]’s test & 0.7741 & 59.9799 & 34.4237 & 56.8556 & 87.4809 & 57.8067 \\ (\(p\)-value) & (0.379) & (\(<\)0.001) & (\(<\)0.001) & (\(<\)0.001) & (\(<\)0.001) & (\(<\)0.001) \\ \hline [PERSON]’s h & 0.121 & 0.855 & 0.631 & 0.806 & 1.097 & 1.092 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Statistical significance tests comparing primary encoder and hybrid classifier. Using [PERSON]'s Kappa coefficient, we assessed the agreement between the hybrid classifier and expert opinion for a statistical measure of their agreement [67]. The testing focused on diagnosing the most challenging cases of AMD, involving thorough analysis and comparison with an assessment scale.