Prediction and Diagnosis of Cardio Vascular Disease -- A Critical Survey

• Published in: 2014 World Congress on Computing and Communication Technologies pp. 246 - 251
• Main Authors: Mohan, K. Raj, Paramasivam, Ilango, Narayan, Subhashini Sathya
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.02.2014
• Subjects: Classification; Classification algorithms; Clustering; Data mining; Decision trees; Diagnosis; Diseases; Educational institutions; Heart; Myocardium
• DOI: 10.1109/WCCCT.2014.74

Cardiovascular diseases related · Coronary heart disease, Angina pectoris, congestive heart failure, Cardiomyopathy, congenital heart disease are the first cause of death in the Asian world. The health care industry collects a huge amount of data which is not properly mined and put into optimum use resulting in these hidden patterns and relationships often going unexploited. Advanced data mining modeling techniques can help overcome these conditions. The health care knowledge management, especially in heart disease, can be improved through the integration of data mining with decision support system. Almost 60% of the world population fall victim to the heart disease. Heart disease management is a complex task requiring much experience and knowledge. Traditional way of predicting heart disease is through physician's examination or a number of medical tests such as ECG Stress test, Heart MRI, CT etc., Computer based information along with advanced data mining techniques are used for appropriate results. The main aim of this study is to detect the various causes of cardiovascular diseases by means of machine-learning techniques with the help of clinical diagnosis. For detecting these image analysis data is used. The aim of this research work is to develop a framework for detecting causes by means of data mining and machine-learning techniques.