## 5 Conclusions  
ML methods have become increasingly important for improving the performance of prediction models that could support decision-making. However, although these approaches have been applied in real-world scenarios, the class imbalance problem is a significant drawback in the development and performance of ML models. The main challenge lies in the fact that skewed class distributions hinder the proper learning process. In the clinical setting, most of the populations being studied are undersampled compared to healthy individuals, which limits the application of ML models. The use of synthetic data in conjunction with real data allows us to address these issues, improving the performance of ML models and achieving a better NCD prediction. In this paper, we evaluated the effectiveness of oversampling techniques (especially GAN-based approaches) in a scenario of binary classification to identify patients with CVD. Experimental results showed that the combination of a GAN-based model (CTGAN) and LASSO achieved an AUC value of 71%,outperforming the results of other oversampling strategies and the match-point method of the undersampling strategy. These advances in health informatics could help with clinical decisions, potentially changing the course of a chronic disease or health condition. Finally, the favorable impact of these decisions on cost savings and patient satisfaction would be significant from both clinical and socioeconomic standpoints. Conceptualization, I.M.-J. and C.S.-R.; methodology, I.M.-J. and C.S.-R.; software, C.G.-V. and D.C.-M.; validation, I.M.-J., H.F. and C.S.-R.; formal analysis, I.M.-J. and C.S.-R.; investigation, C.G.-V. and D.C.-M.; resources, I.T.G., C.G. and C.S.-R.; data curation, I.T.G., M.-L.L. ; writing--original draft preparation, C.G.-V., D.C.-M., H.F. and C.S.-R.; writing--review and editing, I.M.-J., I.T.G., M.-L.L., C.G. and C.S.-R.; visualization, C.G.-V., and D.C.-M.; supervision, I.M.-J. and [PERSON]; project administration, C.G. and [PERSON]; funding acquisition, C.S.-R. All authors have read and agreed to the published version of the manuscript. This work has been partly supported by European Commission through the H2020-EU.3.1.4.2., European Project WARIFA (Watching the risk factors: Artificial intelligence and the prevention of chronic conditions) under Grant Agreement 101017385; and by the Spanish Government by the Spanish Grants BigTheory (PID2019-106623 RB-C41/AEI/ 10.13039/501100011033), and AAVIS-BMR (PID2019-107768 RA-100/AEI/10.13039/501100011033); the Community of Madrid (YEI grant TIC-11649); Project Ref. 2020-661, financed by Rey Juan Carlos University and Community of Madrid; and by the Research Council of Norway (HELSE-EU-project 269882). This work was completed while [PERSON] was beneficiary of the FJC2020-043474-I funded by MCIN/AEI/10.13039/501100011033 and by the European Union \"NextGenerationEU/PRTR\". Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data can be provided under a reasonable official request. All authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The following abbreviations are used in this manuscript: