A systematic review on advanced techniques for early identification of cardiovascular disease

Abstract

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating the development of advanced techniques for its early identification. This paper reviews the latest methodologies and technologies designed to enhance early detection and diagnosis of CVD, thereby improving patient outcomes. It explores the integration of artificial intelligence (AI) and machine learning (ML) algorithms in analyzing medical imaging and patient data, the application of biomarkers and genetic profiling, and the use of wearable technology for continuous monitoring. Additionally, the paper discusses the role of big data analytics in predicting disease onset and progression. By synthesizing current research and clinical practices, this review highlights the potential of these advanced techniques to revolutionize CVD identification, paving the way for more personalized and timely interventions. The findings underscore the importance of multidisciplinary approaches and the need for ongoing innovation in the fight against cardiovascular disease.