A Comprehensive Survey on Cardiovascular Disease

  • Kshitij Tripathi, Hitendra Garg, Himanshu Sharma


Data Mining and Machine Learning is an excellent tool to discover and excavate valuable knowledge from the vast collection of data that can help to understand and establish business-related decision-making trends. Speaking about the medical sector, the application of data mining can generate useful trends and knowledge to be identified and retrieved in this sector that can be helpful in carrying out the clinical diagnosis. This paper will be focusing on Cardiovascular Disease (CVDs) with previous information and data. CVD in the developed countries are a major cause for adult disability and it is one of the leading causes of death in the world. That’s why forecasting CVDs threats correctly and promptly is extremely necessary. The best and most effective means of treatment of CVD are surgical procedure, but studies show that it is not only costly but often has side effects. This research is intended to present a structural review of the machine learning techniques in the cardiovascular dataset. This study analyzes the selected documents and identifies gaps in the existing literature and assist researchers who plan to use machine learning or data mining in cardiac datasets.