## 5 Conclusions  
In conclusion, our exploration into CVD prediction using real-life datasets, encompassing both ML and deep learning approaches, has yielded pivotal insights with profound implications for healthcare practitioners and researchers. Our findings underscore the inherent challenges of dealing with imbalanced datasets and emphasize the critical importance of accurately identifying positive cases, particularly in scenarios where their representation is limited. Initial observations revealed a discernible trade-off between high accuracy and poor recall for positive cases, primarily attributable to dataset imbalance. This spurred an in-depth investigation into remedial measures, leading to the implementation of optimization strategies, such as hyperparameter tuning and resampling techniques. By employing oversampling methods such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN), alongside hybrid techniques like SMOTE-ENN, we effectively balanced the distribution of classes, thereby enhancing the performance of our predictive models. Through meticulous experimentation, we conducted five distinct experiments for each resampling algorithm, leveraging the resampling techniques alongside six ML models and an ANN. These interventions yielded significant enhancements, notably improving recall rates, with optimized boosting algorithms and hybrid resampling methods achieving impressive results. Notably, CatBoost coupled with SMOTE-ENN achieved a remarkable 88% recall rate. Moreover, the integration of ANNs demonstrated their efficacy in handling structured imbalanced data, further enhancing the detection of positive cases in the healthcare domain. Additionally, the indispensable role of electronic health records (EHRs) in predictive analytics for healthcare was emphasized, highlighting the wealth of information within EHR that enhances model predictive capabilities and facilitates a comprehensive understanding of individual health profiles. There are several promising avenues for further exploration, building upon the groundwork laid in our study. Firstly, as previously mentioned, the quality of EHR can exert a substantial influence on model performance. Integrating more diverse and extensive datasets holds potential to augment the generalizability of predictive models, thereby enhancing their capacity to identify high-risk patients. Moreover, delving into the incorporation of genetic and biomarker data could furnish a more comprehensive understanding of CVD risk, given compelling evidence suggesting a potential correlation [2]. Venturing into advanced ML techniques and deep learning architectures presents another avenue for advancement. Employing transfer learning holds promise in significantly bolstering the predictive capabilities of our models. Leveraging insights gleaned from pre-trained models in health-related domains on structured data could substantially refine CVD risk prediction. Additionally, exploring explainable AI methods stands to mitigate interpretability challenges associated with complex models, fostering greater trust and adoption in clinical settings. This could further empower healthcare practitioners by elucidating the reasoning behind the models' decision-making processes. Conceptualization, K.-V.T. ; methodology, K.-V.T. and G.P. ; software, K.-V.T. and G.P. ; validation, K.-V.T., G.P., and C.T. ; formal analysis, K.-V.T. and G.P. ; investigation, K.-V.T. and G.P. ; resources, C.T., K.-V.T., and G.P. ; data curation, K.-V.T. and G.P. ; writing--original draft preparation, K.-V.T. and G.P. ; writing--review and editing, K.-V.T., G.P., and C.T. ; visualization, K.-V.T. ; supervision, C.T. ; project administration, C.T. and K.-V.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Data Availability Statement:** Data are partly available within the manuscript.