Predictive Analytics of Cardiovascular Disease Using Machine Learning

It is estimated that cardiovascular disease (CVD) is responsible for almost 18 million fatalities around the world, making it the top cause of mortality even today. When it comes to reducing death, rates caused by cardiovascular disease, early detection and accurate risk assessment are absolutely necessary. Currently available advancements in machine learning (ML) and deep learning (DL) have the potential to significantly improve the accuracy of both the prediction and diagnosis of cardiovascular diseases. This in-depth analysis investigates the use of a variety of machine learning and deep learning approaches in the field of predictive analytics for cardiovascular disease (CVD). These techniques include decision trees, support vector machines, random forests, neural networks, and convolutional neural networks. These techniques show promise in terms of improving diagnosis accuracy and making it possible to implement interventions that are more effective, which will ultimately contribute to the global effort to reduce the burden of cardiovascular disease.