Real-Time Detection of Cardiac Arrest Using Deep Learning

Abstract

The leading cause of death worldwide is cardiac disease, which kills an estimated 27.9 million people each year and is responsible for 31% of all fatalities. Heart failure is frequently brought on by cardiovascular problems. It can be identified by the heart's inability to deliver enough blood to the body. All of the body's fundamental functions are affected when there is insufficient blood flow. Heart failure is a condition or set of symptoms that weakens the heart. Three important aspects form the foundation of the research study's main results. Given that it essentially measures the efficiency of the heart, this is to be expected. The patient's age is the last factor that is most closely associated. The heart's performance progressively deteriorates with age. The data was modeled using machine learning and ANN with an accuracy of about 80%, showing how effective the framework is at detecting cardiac arrest. Deep learning models' accuracy might rise to 90–95%.