## 4 Discussion  
In this study, we have successfully extended a previously developed partially synthetic data generation methodology to create fully synthetic datasets across three different domains. Building on the UMAP-based generation algorithm, we achieved not only complete synthetic datasets but also significant improvements in data quality, as vali  
Figure 13: ML performance metrics (accuracy, F1-score, and AUC) for support vector machine model trained with real, synthetic, and augmented data. dated against state-of-the-art methods such as CTGAN, CopulaGAN, and TVAE. Our findings demonstrate that the UMAP-based method outperforms these benchmarks in terms of fidelity, particularly for breast cancer and cardiovascular disease data. Moreover, the synthetic and augmented datasets generated using the UMAP-based approach exhibit high utility, as evidenced by enhanced machine learning model performance across different models. In terms of fidelity, the UMAP-based method outperformed other state-of-the-art techniques in most features across the datasets, as assessed by the Kolmogorov-Smirnov (K-S) test for cumulative distribution function evaluation. While the method consistently achieved high fidelity across all domains except for prostate cancer data, the differences in K-S statistics between UMAP and other methods were generally slight for most attributes. Nonetheless, the results underline the competitive performance of the UMAP-based methodology in ensuring fidelity while generating synthetic data. The UMAP-based synthetic and augmented datasets also demonstrated promising results in terms of utility. Across most machine learning models and dataset domains, the UMAP-generated data exhibited enhanced predictive capabilities compared with models trained exclusively on real data. Of particular note is the performance of the SVM model with cardiovascular data, where the UMAP-based synthetic datasets outperformed all other datasets, including real, synthetic, and augmented ones. In other cases, TVAE datasets also showed strong performance, often comparable to the UMAP-based data. Nevertheless, it is important to note that, while model performance improved with synthetic data, the gains remain moderate, suggesting that further tuning or larger reference datasets could further optimize model outcomes. As highlighted in the results analysis, augmented datasets often yield better utility outcomes compared with real or synthetic datasets alone. Data augmentation serves as a powerful tool for increasing both the diversity and volume of training data, thereby enhancing model performance. An intriguing direction for future research would be to systematically analyze the impact of different augmentation ratios, aiming to identify the optimal balance between real and synthetic data for various machine learning tasks. An interesting finding is the low correlation observed between fidelity and utility metrics. Higher fidelity, as measured by the Kolmogorov-Smirnov statistic, does not necessarily correspond to better utility metrics in machine learning tasks. This observation was further validated through a correlation analysis between fidelity and utility results. The Pearson correlation coefficient between these properties for prostate cancer data was 0.185, for breast cancer data 0.033, and for cardiovascular diseases 0.34, all indicating weak relationships between these two properties. This discrepancy can be attributed to the fact that fidelity metrics assess the alignment of individual attribute distributions between synthetic and real data. However, faithfully reproducing the marginal distributions of all attributes does not guarantee that the synthetic data will capture the joint distribution of the original dataset. This highlights the importance of selecting evaluation metrics tailored to the intended application of the synthetic dataset.