Heart Disease Detection using Data Mining

Data Mining refers to using a variety of techniques to classify the propose of information or decision making knowledge in the database .The healthcare industry collects huge amounts of healthcare records which, unfortunately, are not "mined" to discover hidden information for effective decision making .The data from medical history has been found as diverse data and it seems that the various forms of data should be interpret to envisage the heart disease of a patient. Various techniques in Data Mining have been applied to predict the patients of heart disease .Using data mining classification techniques such as Decision Tree Algorithm, Naïve Bayes, and Neural Network. etc., the patient risk level is classified .Accuracy of the risk level is high when using more number of attributes. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals .It can predict the likelihood of patients getting a heart disease using medical profiles such as age, sex, blood pressure and blood sugar.