## 3 Results  
The hybrid classifier's initial stage of training involved training the global classifier using four selected base classes: N, P, S', and V'. The results from testing the global classifier are presented in the error matrix displayed in the accompanying Figure 5 and Table 4.  
Testing of the global classifier revealed a significant reduction in class imbalance. It increased accuracy in P stage detection, suggesting that integrating the global classifier and regressor responses further improves the P detection process. The values of the regressor were divided into three regions:  
* P-free region (0-0.3);
* Region of initial P progression (0.3-0.67);
* Region of late P progression (0.67-1). If the regressor's response falls outside the first interval and the global classifier indicates that the class S' closest to P is the correct one, then the final response is determined to be P.  
In the next stage of developing a hybrid classifier, we trained binary classifiers to separated the classes S and SL, as well as V and VI. The results of testing these classifiers \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **N** & **P** & **S’** & **V’** \\ \hline \multirow{2}{*}{Precision} & 0.9182 & 0.7961 & 0.8350 & 0.9244 \\  & (0.901, 0.935) & (0.771, 0.821) & (0.812, 0.858) & (0.908, 0.941) \\ \hline \multirow{2}{*}{Sensitivity} & 0.9528 & 0.8632 & 0.7414 & 0.9322 \\  & (0.940, 0.966) & (0.842, 0.884) & (0.714, 0.769) & (0.917, 0.948) \\ \hline \multirow{2}{*}{Specificity} & 0.9726 & 0.9382 & 0.9467 & 0.9716 \\  & (0.962, 0.983) & (0.923, 0.953) & (0.933, 0.961) & (0.961, 0.982) \\ \hline \multirow{2}{*}{F1-score} & 0.9352 & 0.8283 & 0.7854 & 0.9283 \\  & (0.920, 0.9531) & (0.805, 0.8541) & (0.761, 0.818) & (0.912, 0.944) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance indicators of the global classifier with confidence intervals. Figure 5: Error matrix of the global classifier. are illustrated as error matrices in Figures 6 and 7, and their performance indicators are provided in Table 5.  
The obtained data indicate that binary classifiers are effectively separate classes, S and SI and V and VI, as the metrics show consistently high values, even for adjacent classes. The regressor was trained using a partially labeled dataset, with 80% of the data used for training. The dataset included three states: 0--no P, 0.5--the initial stage of P, and 1--the extreme P stage. The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used as metrics to evaluate the regression model. MAE calculates the average error in a set of forecasts and is less affected by outliers. On the other hand, RMSE calculates  
Figure 6: Error matrices for binary classifier S and SI. Figure 7: Error matrices for binary classifier V and VI. the square root of the average of the squared differences between the predicted and actual values. It is more sensitive to large deviations in the predicted value, allowing it to clearly emphasize significant discrepancies between the predicted and actual values [66]. First, the regressor was trained on these examples, and then the LPA model. The results of testing the regressor are presented in Table 6.  
Concerning the boundaries we set for intermediate AMD progression, the outcome achieved using a 1/4 dataset can be minimally acceptable. For subsequent work, we utilized a model trained on a dataset with a 1/3 labeled data ratio. The final stage of our study involved a comprehensive testing of the entire hybrid classifier. This testing was crucial in evaluating the performance of our system, and the results are presented in the error matrix in Figure 8 and Table 7.  
To assess progress in improving the classification of the entire set of AMD stage classes, the McNemar test was conducted, and the Cohen effect size measure h was applied to compare the outcomes of the source encoder and the hybrid classifier, as presented in Table 8.