## 6 Recent Trends in Machine Learning for Cardiovascular Disease Prediction and Future Works
To address these issues, the idea of federated learning is introduced, where the focus is on sharing model knowledge instead of sharing raw data [216]. Federated learning establishes a more secure system by enabling model training on decentralized devices, preserving privacy, and minimizing the risk of centralized data breaches [217]. This approach results in a robust system with enhanced security and data access controls that safeguard privacy [218]. A suggested future work would involve the development and implementation of a standardized, interoperable, and secure federated learning framework for data sharing. This framework would allow multiple healthcare institutions and researchers to collaborate while preserving patient privacy [219]. * The management of vast historical data and the constant influx of streaming data in healthcare services pose a formidable challenge for conventional database storage and machine learning approaches. Addressing this challenge in real-time data processing has led researchers to explore big data approaches [220; 221; 222]. Further research studies in this direction are needed. * Machine learning involves predictive models that analyze patient data for early detection. Future research should refine predictive models for early anomaly detection and develop advanced algorithms for more accurate predictions. Efforts should be focused on creating personalized care plans and improving machine learning integration into telemedicine platforms for informed decisions during remote consultations. * Future work should address the ethical concerns issue in healthcare systems. In particular, Strategies to mitigate bias and enhance fairness in healthcare systems should be developed. This includes the development of novel algorithms and methodologies that prioritize equity and fairness. * Achieving the best possible level of security and privacy protection is important, which need to be considered in the future [223]. Data sharing and collaboration among healthcare institutions are crucial for building large, diverse datasets. This collaboration allows the development of more accurate and robust machine-learning models for medical applications. However, ensuring data privacy, handling data heterogeneity, and addressing issues like missing values and imbalanced datasets is crucial, and should be investigated in the future. In addition, collecting and preprocessing healthcare data for cardiovascular disease prediction is still a challenging issue that needs to be addressed. * As contemporary technology continues to advance, the acquisition of high-resolution and multidimensional data becomes increasingly feasible. In dealing with such high-quality data, the conventional machine-learning approaches may exhibit some limitations. Exploring the potential of employing a blend of multiple machine learning models could prove to be a promising avenue for addressing the challenges posed by high-dimensional data in future research. * To advance the application of machine learning in healthcare systems, future work should focus on establishing strong collaborative networks between data scientists, clinicians, and domain experts [224]. This collaboration will ensure that machine learning models are designed and tuned to enhance patient care and decision-making. * Future research should emphasize the potential of machine learning to address cardiovascular disease on a global scale, with a specific focus on regions with limited access to healthcare resources. * Future research should also aim to bridge healthcare disparities by providing accessible and effective cardiovascular disease prediction and management tools to underserved populations. * Artificial General Intelligence (AGI), or strong AI, mimics human intelligence by learning, reasoning, generalizing, and exhibiting self-awareness [225; 226]. AGI may have a significant impact on cardiovascular disease, due to its adaptability in accomplishing diverse tasks, integrating medical knowledge, and personalizing treatment planning [227].