## 5 Conclusions  
This study presents a reliable and versatile tool for generating synthetic patient cohorts that mirror real-world data structures. It provides a structured simulation framework that maintains control over input assumptions, enforces logical coherence, and supports expert-driven modeling when empirical data are unavailable. The results emphasize that including population-relevant correlations, especially those informed by clinical experts, substantially improves the realism and fidelity of synthetic datasets. The tool is not only technically sound but also accessible to healthcare professionals with limited statistical or coding experience, making it highly applicable across diverse research and policy contexts. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/a18080475/s1](https://www.mdpi.com/article/10.3390/a18080475/s1)]([https://www.mdpi.com/article/10.3390/a18080475/s1](https://www.mdpi.com/article/10.3390/a18080475/s1)), S1 Summary statistics of means, standard deviations, parameter bounds, and pairwise correlations. Conceptualization, A.N.F. and K.E. ; methodology, A.N.F., R.A., K.E., B.N. ([PERSON]), D.N., B.N. ([PERSON]) and Z.V. ; software, A.N.F., R.A., K.E., B.N. ([PERSON]), D.N. and B.N. ([PERSON]); validation, A.N.F., R.A., A.I. and B.N. ([PERSON]); formal analysis, A.N.F., R.A. and K.E. ; investigation, A.N.F., R.A. and K.E. ; resources, A.N.F. and K.E. ; data curation, A.N.F., R.A. and K.E. ; writing--original draft, A.N.F., R.A., R.H. and A.I. ; writing--review and editing, A.N.F., R.A., R.H., K.E., A.I, B.N. ([PERSON]), D.N., B.N. ([PERSON]) and Z.V. ; visualization, A.N.F., R.A. and A.I. ; project administration, A.N.F. and R.H.; supervision, R.H., B.N. ([PERSON]) and Z.V. ; funding acquisition, R.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This study used publicly available, de-identified data (NHANES 2021-2023, Framingham Heart Study) to generate synthetic patient cohorts for validation. No human subjects were involved, and ethical approval was not required. Data handling followed legal and ethical guidelines. Synthetic data, created via statistical simulation, cannot be linked to real individuals. The real-world datasets used for validation (NHANES and Framingham Heart Study) are publicly available from the U.S. Centers for Disease Control and Prevention ([[https://wwwn.cdc.gov/nchs/nhanes/continuoushanes/default.aspx?Cycle=2021-2023](https://wwwn.cdc.gov/nchs/nhanes/continuoushanes/default.aspx?Cycle=2021-2023)]([https://wwwn.cdc.gov/nchs/nhanes/continuoushanes/default.aspx?Cycle=2021-2023](https://wwwn.cdc.gov/nchs/nhanes/continuoushanes/default.aspx?Cycle=2021-2023)); accessed on 29 July 2025) and the National Heart, Lung, and Blood Institute ([[https://www.kaggle.com/code/dessanriv/framingham-heart-study](https://www.kaggle.com/code/dessanriv/framingham-heart-study)]([https://www.kaggle.com/code/dessanriv/framingham-heart-study](https://www.kaggle.com/code/dessanriv/framingham-heart-study)); accessed on 29 July 2025). The synthetic datasets generated during the current study are available from the corresponding author upon reasonable request. The authors acknowledge the U.S. CDC and NHLBI for maintaining and providing access to the NHANES and Framingham datasets, respectively. [PERSON] is an employee of Syreon Middle East. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: