Diagnosis of Heart Disease Using Data Mining Technique

Shinde S. B., Amrit Priyadarshi

Abstract: Data mining techniques have been widely used in clinical decision support systems for prediction or diagnosis of various diseases with accuracy. These techniques are used to discover hidden patterns and relationships from the hospital data. One important applications of data mining technique is to diagnose the heart diseases because it is one of the reasons for deaths over the world. Almost all systems which predict heart diseases, use medical dataset as inputs like age, sex, cholesterol, blood sugar etc. There is no system which predicts heart diseases based on the attributes such as diabetes, family history, tobacco smoking, intake of alcohol, obesity, hypertension or any other physical inactivity etc. Heart disease patients have lot of these visible risk factors in common which can be used very effectively for detecting. System based on the risk factors would not only help medical professionals but also it would give patients a warning about the probable presence of heart disease even before he visits a hospital or goes for costly medical Checkups. Hence this system presents a technique for prediction of heart disease. These techniques involve one successful data mining technique named Nave Bayesian algorithm. It also provides the training tool for nurses and medical students to predict patient having heart disease.

Keywords: Heart Disease, Data Mining, KDD, Decision Support and Nave Bayes