Access to medical data is often restricted due to privacy and security policies. The generation of synthetic data from real data is a widely adopted technique to address these limitations. This research presents the enhancement of a patient-centric methodology for generating synthetic data, specifically designed for patients diagnosed with Chronic Kidney Disease (CKD), by comparing the results with synthetic data generated using algorithms from the Synthetic Data Vault (SDV) library. The key advantage of the methodology proposed by the authors lies in its explainability and the traceability of the results, as it relies on statistical methods and data analysis rather than AI algorithms. The MIMIC-III clinical dataset serves as the foundation for generating synthetic patients in this study. This article outlines the data preprocessing and filtering applied to this dataset. Subsequently, synthetic data for CKD patients is generated using the proposed methodology. A comparison is then made between the synthetic data and the real data. Furthermore, the synthetic data is compared with the results obtained using the AI algorithm known as SMOTE. Finally, an extensive comparative analysis is conducted with the results obtained using the Gaussian Copula and CTGAN algorithms from the SDV library.