An Automated GRU Model for Classification of CVD Using RDO Optimization

Abstract

Cardiovascular disease (CVD), also known as heart disease, remains the primary cause of death globally, responsible for an alarming 22 million deaths in 2022 alone. This figure underscores a significant global health crisis, as more than 80% of these deaths are attributable to CVD-related complications. This research paper delves into the multifaceted aspects of CVD, including its epidemiology, risk factors, pathophysiology, and the socio-economic burden it imposes. We examine the primary contributors to the high mortality rate, such as hypertension, diabetes, obesity, and lifestyle factors, while also exploring the latest advancements in prevention, diagnosis, and treatment. Furthermore, the paper highlights the disparities in CVD outcomes across different populations, emphasizing the urgent need for tailored public health strategies and interventions. By synthesizing current research and clinical data, this study will  provide a complete and contrast  overview  of the CVD, fostering a deeper understanding and paving the way for more effective global health policies and practices.

The GRU-based model for cardiovascular disease prediction is employed in the article that is being presented. This method uses advanced deep learning and the Convolutional Gated Recurrent Unit Network model to predict Cardiovascular Disease in any patient. The outcome of this particular procedure yields better results than those of the methods that are already in use.