###### Abstract
Not all synthetic datasets precisely replicate the content and properties of the original data, which may impact their applicability across different applications [18]. Despite these challenges, the integration of synthetic data into AI model development has significant potential to advance CDSSs, while protecting patient privacy. In this paper, we introduce an approach that aims to predict high-risk cardiovascular and cerebrovascular events in hypertensive patients well before their occurrence. Our work involved developing a custom DNN specifically tailored for detecting atrial fibrillation (AF), using a small dataset obtained from a single hospital. To address the limitations posed by the small size of the dataset, we used synthetic data generation techniques to augment our training set. Furthermore, we extended our investigation by applying TL techniques, where parts of the weights obtained from the deep learning (DL) model trained for AF prediction were utilized to train another model tasked with detecting cardiovascular events (CE). By integrating DL with advanced data generation methods, our study aimed to demonstrate the effectiveness of this combined approach in enhancing predictive accuracy and robustness in healthcare applications. To summarize, our goal is to equip clinicians with innovative technological solutions for predicting adverse cardiovascular outcomes. Exploiting deep learning and synthetic data generation, our approach addresses the challenges of data sharing, enhancing model quality, while preserving patient privacy. With our approach, the similarities between common and rare CVDs can be exploited to tackle the significant problem of the small datasets for the latter class of patients. This aspect will boost the quality of models developed for the prediction and management of CVDs in general. Moreover,with a validated and safe infrastructure, it will be possible to share valuable insights about the features of disease patients, without the risk of exposing their real data; this will further improve the efficacy of CDSSs, lowering the burden on the economy and personnel of healthcare systems. We had the unique opportunity to work with a locally available dataset containing Holter ECG data from patients who developed atrial fibrillation (AF). Leveraging this dataset, we aimed to explore its potential in predicting rarer and more severe cardiac events. To extend the analysis, we incorporated a widely recognized dataset containing detailed HRV features from patients who had experienced different cardiac and cerebrovascular events, including 11 myocardial infarctions, three strokes, and three syncopal episodes. This work addresses critical gaps in cardiovascular risk prediction by leveraging advanced synthetic data generation and transfer learning methods to enhance model performance on limited datasets. By tailoring deep learning architectures for atrial fibrillation and cardiovascular event classification, this approach not only achieves robust accuracy but also addresses the challenges of data scarcity and privacy in healthcare. These innovations promise significant practical implications, including improving clinical decision support systems, optimizing resource allocation, and ultimately advancing patient care through early risk stratification and targeted intervention strategies.