###### Abstract  
In this comprehensive study, we employed a versatile approach to tackle the prediction challenges associated with atrial fibrillation (AF) and cardiovascular events (CE). Exploiting the Gaussian copula synthesizer technique for data generation, we created high-quality synthetic data to overcome the limitations posed by scarce patient records. Heart rate variability (HRV), known to be an efficient indicator of cardiac health often used with artificial intelligence (AI), was used to train and optimize custom-built deep learning (DL) models. Additionally, we explored transfer learning (TL) to enhance the model capabilities by adapting our AF classification model to address CE classification challenges, effectively transferring learned features and patterns, without extensive retraining. As a result, our models achieved accuracy rates of 77% for AF and 82% for CEs, with high sensitivity, highlighting the efficacy of synthetic data generation and transfer learning in improving classification performance across diverse medical datasets. These findings hold significant promise for enhancing diagnostic and predictive capabilities in clinical settings, ultimately contributing to improved patient care and outcomes. s +
Footnote †: journal: Applied sciences  
article  
[color=gray!20, linecolor=gray! than EUR 600 million. Regarding cardiovascular diseases in general, The American Heart Association projected that the total cost of cardiovascular diseases, including myocardial infarction (MI), in the United States will rise from USD 627 billion in 2020 to USD 1851 billion by 2050, driven by a near quadrupling of healthcare costs [11]. In the European Union, cardiovascular diseases (CVDs) cost approximately EUR 282 billion annually, meaning EUR 630 per EU citizen [12]. Heart rate variability (HRV) and electrocardiography (ECG) are essential methods for diagnosing and managing a wide range of cardiovascular, cerebrovascular, and related diseases. ECG is simply the recording of the bio-electrical activity of the heart. HRV is a measure of the variation in cardiac rhythm; usually a heart that has a higher HRV represents the ability of the subject to adapt rapidly to different stimuli and is correlated with a healthier condition [13]. HRV analysis has evolved a lot over time, and, to date, there are many indices that can be extracted from an ECG record. Time-based, frequency-based, and non-linear features can be easily calculated from a heart rate record, where each of these features represents a different aspect of the HRV and different values have been correlated with different outcomes [14]. These techniques provide detailed insights into the autonomic nervous system, offering crucial prognostic information that can guide clinical decisions and improve patient outcomes [15]. Given the high variability and number of patients, AI-based systems could help physicians in the difficult task of monitoring patients and deciding on the best treatment approach. Developing AI-driven clinical decision support systems (CDSS) requires extensive patient data to ensure accuracy and reliability. However, this requirement introduces significant privacy challenges, as the reliance on large datasets amplifies concerns about data security, informed consent, and the potential for unauthorized exploitation of sensitive information [16]. Synthetic data offer a promising solution to these privacy concerns. By generating artificial datasets that mirror the statistical properties of real patients, synthetic data enable the development and validation of CDSSs without exposing personal health information. This approach facilitates data sharing and collaboration across institutions, while adhering to stringent privacy regulations [17]. However, it is important to acknowledge that synthetic data have limitations, including the potential risks of data leakage and dependency on imputation models.