## 6 Recent Trends in Machine Learning for Cardiovascular Disease Prediction and Future Works  
This section provides a list of some open problems and suggestions regarding the application of machine learning in cardiovascular disease prediction and highlights some further suggestions that can be considered for future work. * Delays in diagnosing cardiac disease continue to have a major impact on the treatment of patients [123]. More efficient classification and early prediction of cardiovascular disease methods are required. * Deep learning techniques have gained prominence in medical image analysis and ECG data interpretation due to their significant role in providing more accurate diagnosis and prediction of diseases [31]. However, the adoption of such techniques in clinical practice comes with some challenges. These challenges include data privacy, the need for large and diverse datasets, regulatory compliance, model robustness, and generalization issues [203]. Therefore, future research should address these challenges. Furthermore, the interpretability of deep learning models remains an ongoing research topic, as understanding the inner workings of complex neural networks is crucial for clinical acceptance. * Explainable AI (XAI) is essential in cardiovascular disease prediction, attracting significant research attention [204]. XAI techniques empower clinicians by elucidating the importance of each feature in predictions, enabling informed decision-making and building trust [187]. Collaboration between healthcare providers and AI-driven systems can be facilitated through XAI, translating complex model outputs into actionable insights and enhancing clinicians' diagnostic and treatment capabilities. Further research studies in this essential direction are needed. * The transformative impact of IoT technology on healthcare is evident, enabling remote patient monitoring and facilitating telemedicine through wireless sensors [205; 206; 207]. The integration of machine learning models with wearable devices, IoT sensors, and mobile health applications for continuous cardiovascular disease monitoring represents a compelling research avenue [32; 208]. Exploring the efficiency of remote medical applications by employing machine learning algorithms is crucial for enhancing data analysis, increasing reliability, and achieving fast and accurate decision-making. Further research in this domain is essential for advancing healthcare systems. * Machine learning can enhance risk assessment and treatment recommendations by analyzing the historical data of patients [209; 210]. As such, machine learning can identify early warning signs of cardiovascular disease, enabling timely interventions [211; 212]. Therefore, future works should focus on developing more accurate models to enhance risk assessment and treatment recommendations. * The Generative Adversarial Network (GAN) stands out as a widely embraced technique in the machine learning domain [73; 213]. Leveraging this method enables the creation of synthetic data closely resembling real data, making GAN a promising solution for addressing challenges associated with data scarcity in cardiovascular discussions. Hence, understanding the intricate connections between GAN and data privacy could be explored in future studies. Besides, utilizing GAN to classify unbalanced signals in fetal cardiovascular rate data can be investigated in future [214]. * Efforts are being made to improve interoperability and data sharing among healthcare systems and institutions [215]. The ability to access and share medical data is crucial for training efficient machine learning models. Research is ongoing to improve the robustness and generalization of machine learning models in healthcare systems. Future research could involve further investigation into interoperability and data sharing among healthcare systems. * Aggregating larger data at a central server confronts issues.