## 1 Introduction
However, obtaining a labeled dataset can be a complex task requiring significant time investment for high-quality labeling [38]. Open-labeled datasets are only sometimes suitable for training ML models since the available OCT image datasets may not correspond to the imaging specifics of different tomographs [39; 40]. Unsupervised learning segmentation methods also require expert participation to verify the results, which can be difficult [41]. In cases where obtaining a labeled dataset for segmentation is difficult or economically unfeasible, biomarker-based classification can be achieved by directly applying classifiers to images using additional predictor analysis algorithms. This approach does not involve identifying biomarker boundaries to assess their progression, but instead focuses the diagnostic's attention on the presence of a group of biomarkers. It allows the expert to concentrate on specific areas of the image to confirm or refute the hypotheses proposed by the algorithm. This approach aligns with the concept of decision support systems. An algorithm error is less likely to result in an incorrect decision by an expert than the allocation of pathology segments, which can confuse an expert and complicate the determination of the actual boundaries of pathologies [42; 43]. Addressing several associated challenges is essential to creating clinical decision support systems (CDSSs) for adjusting the treatment approach for retinal diseases using the AMD stage classifier. Identifying the intermediate stage of AMD is particularly challenging because the OCT images at this stage can resemble those from both the early and late stages of AMD. One effective approach to tackling this issue is to utilize the relationship between the identified classes of disease stages when implementing a hybrid classification [44]. Hybrid classification involves combining multiple ML models or methods to improve classification efficiency. This approach takes advantage of different algorithms to address specific shortcomings or limitations that one model may have [45]. Hierarchical classification can help address issues like class imbalance and overfitting when analyzing the structure of medical data. This method organizes the class space as a hierarchy, often represented as a tree or a directed acyclic graph [46]. Once the hierarchical tree is constructed, the ML model's work can be divided into tasks for each tree branch. This approach allows for breaking down a complex problem into more manageable subtasks. Thus, ref. [47] demonstrated that organizing classes into a hierarchy can significantly enhance the scalability of the classification process. This breakthrough paves the way for decision-making at different levels and simplifies the complexity of distinguishing many classes simultaneously. Moreover, ref. [48] showed that hybrid and hierarchical classification can effectively organize the feature space, mainly when dealing with many classes. In addition, the authors of [49] demonstrated that hierarchical classification allows for identifying complex relationships between classes, which are often present, including in biological data. This leads to more meaningful and accurate classification results. The results of the studies show promise in addressing the various challenges that arise during the development process due to the unique nature of medical datasets and clinical decision support systems. This work aims to develop an OCT-based AMD classification algorithm, which will become part of the CDSS, monitor changes in AMD, and adjust patient management plans accordingly. The main focus of the work was on the intermediate stage of AMD and the ability to assess its progression compared to the early and late stages of the disease. Implementing such algorithms involves preprocessing images and organizing data for training and testing a machine learning model [50]. However, data collection in clinical practice presents several challenges.