## 7 Conclusions  
Under limited data conditions, our experiments demonstrated that synthetic data generated by GANs can effectively support Algorithm selection. In several cases, SAS not only matched but statistically outperformed RAS in final classification accuracy, with significant gains observed for SVM, LogisticRegression, RandomForestClassifier, and MLPClassifier. Importantly, these improvements were consistent across multiple seeds used for GAN and Algorithm selector training. Although DecisionTreeClassifier showed a performance drop when trained on synthetic data, and KNeighborsClassifier exhibited no significant difference, the overall trend supports the viability of synthetic data for Algorithm selection in low-resource settings. The synthetic images we create help us make medical image classification more accurate by using Algorithm selection, achieving better results than just using the best Algorithm or the Algorithm selection trained on the original dataset. While our study primarily used traditional machine learning Algorithms as selectors due to their interpretability and efficiency with limited training data, future work could explore deep neural networks as Algorithm selectors. Deep learning-based selectors might capture more complex patterns in the data that determine Algorithm effectiveness, potentially further improving selection accuracy. However, such approaches would likely require larger training datasets and might introduce additional computational overhead during both training and inference. The trade-off between selector complexity and performance gains represents an interesting direction for future research, particularly in the context of synthetic training data, where generating larger volumes of data is feasible.  
One limitation of our current study is that we investigated Algorithm selection trained on either purely real or purely synthetic data, but did not explore mixed datasets combining both types. Future work could examine how varying the proportion of real to synthetic images in the training data affects Algorithm selection accuracy. Such an investigation might reveal an optimal mixing ratio that leverages the complementary information in both real and synthetic samples. This approach could potentially further improve Algorithm selection performance while minimizing the amount of real data required. Additionally, it would be valuable to study whether certain types of real samples are more informative when combined with synthetic data, which could lead to more efficient strategies for creating hybrid training datasets.  
Conceptualization, M.L. and M.K.; Methodology, F.M. and M.K.; Software, M.Z.; Validation, B.T. and S.N.; Investigation, M.Z. and M.L.; Data curation, M.Z.; Supervision, B.T. and M.L.; Writing--original draft, M.Z. and M.L.; Writing--review and editing, B.T., F.M., M.K. and S.N.; Visualization, M.Z. and M.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was partially funded by \"Nazarbayev University Collaborative Research Proposal #091019 CRP2108 to [PERSON]\" and \"Nazarbayev University Faculty-Development Competitive Research Grant Program for 2023-2025 Grant No. 20122022 FD4109, [PERSON]\".  
**Data Availability Statement:** All data were obtained form [[https://data.mendeley.com/datasets/rscbjbr9](https://data.mendeley.com/datasets/rscbjbr9) sj/2]([https://data.mendeley.com/datasets/rscbjbr9](https://data.mendeley.com/datasets/rscbjbr9) sj/2).  
**Conflicts of Interest:** The authors declare no conflicts of interest.