A Survey Paper On Application Of Deep Learning Techniques For The Study And Analysis Of Cardiovascular Diseases


Cardio Vascular Diseases are group of disorders of the human circulatory system which disturbs the structure and function of the heart and is the number one cause of Cardiac death globally. Any heart conditions that are characterized by the narrowed or blocked blood vessels, structural complications, heart muscles related issues, defected valves, irregular rhythm are termed as Cardiovascular disease. The Cardiovascular diseases include coronary heart disease, cerebrovascular disease peripheral arterial disease, and rheumatic heart disease, congenital heart disease, deep vein thrombosis and pulmonary embolism, myocardial infarction, strokes, congestive heart failure etc.…..These heart diseases are becoming one of the leading causes of the death worldwide. This high death rate can be reduced to an extent by detecting various heart diseases in an early stage which help in providing timely treatment to the patients. As per the reports of World Health Organization in 2012, a total of 17.5 million deaths were reported due to cardiovascular diseases worldwide, which accounts for 31% of global death. By 2030 the yearly death rates due to cardiovascular disease are expected to rise to 22.2 million. In 2010, the global direct medical expenses due to CVDs are approximated to be US$863 billion in total. These expenditures due to CVDs continue to rise and may reach US$20 trillion by 2030. Among CVDs 7.4 million deaths were due to Coronary Artery Disease (CAD). This report from various sources reveals the importance of early detection and diagnosis of CVDs which can save most of the lives. Keywords: Convolution Neural Network, empirical mode decomposition, Cardiovascular disease, deep learning.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL