Machine Learning In Cardiovascular Risk Assesment
Main Article Content
Abstract
Background: Cardiovascular disease (CVD) remains the leading cause of death globally, necessitating advancements in early detection and risk assessment. Traditional tools, such as the Framingham Risk Score, often lack precision and generalizability, particularly for diverse populations.
Objective: To highlight the potential of machine learning (ML) in overcoming the limitations of traditional CVD risk assessment methods and transforming cardiovascular health.
Methods: The introduction provides an overview of ML techniques, including supervised, unsupervised, and reinforcement learning. These methodologies are presented in the context of analyzing complex medical datasets, such as electronic health records (EHRs), genomic data, and imaging, to enable precise and individualized risk predictions.
Results: ML offers sophisticated approaches to improving the accuracy of cardiovascular risk prediction. By leveraging advanced data analytics, it addresses the complexities of personalized care and the limitations of traditional assessment tools.
Conclusion: Machine learning has the potential to revolutionize cardiovascular care by enhancing prediction, prevention, and treatment strategies. This approach promises to significantly reduce the global burden of CVD through more precise and personalized interventions.