Vol. 8, Special Issue 1 (2019)

The review encompasses a wide array of studies published in peer-reviewed journals, exploring diverse ML techniques employed in cardiovascular disease prediction. Various algorithms, including but not limited to support vector machines, random forests, neural networks, and ensemble methods, are scrutinized for their efficacy in leveraging complex datasets to improve risk prediction accuracy. The selected studies cover a broad spectrum of patient populations, ranging from individuals with specific risk factors to large-scale cohort studies, thereby ensuring a holistic evaluation of the diverse applications of ML in cardiovascular risk prediction.

Key findings highlight the promising performance of ML algorithms in identifying subtle patterns and interactions within multidimensional datasets, allowing for more accurate risk stratification. However, challenges such as interpretability, generalizability, and the need for large, diverse datasets are also discussed. The review sheds light on the potential integration of ML models into clinical practice, emphasizing the importance of collaboration between data scientists, clinicians, and researchers to ensure the responsible and ethical deployment of these technologies.

Furthermore, the review addresses gaps in the existing literature and proposes avenues for future research, including the exploration of explainable AI techniques, validation in real-world clinical settings, and the integration of multimodal data sources for improved predictive performance. As the field of ML in cardiovascular disease prediction continues to evolve, this review aims to serve as a valuable resource for researchers, healthcare professionals, and policymakers, fostering a deeper understanding of the current landscape and guiding the development of more robust and clinically relevant predictive models.