## 4 Discussion
For scenarios where the primary goal is to enhance machine learning model performance, as in cases of limited or low-quality training data, utility metrics provide a more relevant measure of synthetic data quality. Conversely, when the objective is to closely replicate the original attribute distributions for statistical analysis, attribute-specific tasks, or other purposes, fidelity metrics, such as CDF comparisons, are more appropriate for quality assessment. For data quality comparison, we selected benchmarks such as CTGAN, CopulaGAN, and TVAE because these are well-established generative algorithms specifically tailored for tabular data, aligning closely with the focus of this study. The selection was carefully made to ensure relevance and provide meaningful insights into the performance of our methodology. However, tabular data generation is a rapidly evolving field, with innovative proposals for tabular data generation such as diffusion models or normalizing flows. Expanding the comparison to include these methods in future studies would be highly beneficial, offering deeper insights into the strengths and limitations of our UMAP-based approach and further contextualizing its performance within the broader landscape of generative models. One key strength of the UMAP-based algorithm is its ability to generate large-scale synthetic datasets, even when the initial reference dataset is relatively small, as was the case with the prostate cancer and cardiovascular diseases datasets. This study successfully scaled fully synthetic datasets to over fifty times the size of the original dataset, illustrating the method's potential for substantial data augmentation. Future work will focus on expanding the algorithm's applicability to larger, more diverse datasets. With bigger and more varied reference datasets, the algorithm can generate even larger synthetic datasets, as the synthetic data size scales exponentially with the reference dataset size. Additionally, we aim to explore the algorithm's performance with datasets containing a higher number of features, which could reveal how synthetic data quality varies across different attributes. Although initially developed for healthcare applications, where data scarcity and privacy concerns are critical, the proposed algorithm is inherently generalizable and can be applied across various domains. The only requirement is the availability of a suitable reference dataset, making this methodology a versatile solution for addressing data limitations in diverse fields. Its reproducibility has already been demonstrated through promising results in three distinct healthcare domains: prostate cancer, breast cancer, and cardiovascular disease. Nevertheless, exploring its application beyond the healthcare domain remains an exciting avenue for future research, potentially broadening its impact and utility. Regarding different domains, future research work could involve testing different dimensionality reduction tools. Although UMAP has shown excellent performance, specially for healthcare data, different fields could benefit from a different dimensionality reduction tool such as t-distributed stochastic neighbor embedding (t-SNE) or TriMap. Although UMAP effectively transforms data with many attributes into a two-dimensional space, the iterative application of UMAP-based transformations for synthetic data generation can introduce computational challenges, particularly when scaling to datasets with a high number of features or large sample sizes. To address these challenges, future work should include a detailed computational efficiency analysis, assessing runtime, memory usage, and scalability. Such an analysis would help quantify the computational trade-offs involved and identify any potential bottlenecks. Optimization strategies, such as parallelization or approximations to accelerate the UMAP computations, should also be considered to enhance scalability. For instance, distributed computing or GPU acceleration could significantly reduce runtime for high-dimensional data.