DATA MINING TECHNIQUES TO DIAGNOSE HEART DISEASES

Abstract

Heart disease is one of the major cause of gruesome and transience in modern society. Though the medical diagnosis is extremely important, complicated tasks should be performed accurately and efficiently. For heart disease further investigation is needed even though diagnosis and treatment are made. There is a huge data available within the healthcare systems. The availability of variousamounts of medical data implies the necessity for powerful data analysis tools to extract useful knowledge. There is a task of effective analysis tools to identify hidden relationships and trends in data. Knowledge discovery and data mining have identified infinite applications in business and scientific field. Heart disease diagnosis is one the applications where data mining tools are proving its successful results. In this research paper, to diagnose heart diseases throughdata mining tools such as, support vector machine (SVM), rough set theory, neural networks, association rules, genetic algorithm. In this paper, it will be examined that decision tree and SVM are the most effective ones for heart disease. So, data mining helps to predict of high and low occurrence of risks in heart diseases.