Detection Of Heart Attack Type Along With Suggestion Of Treatment

The term Heart disease incorporates the diverse disease that have emotional impact on human heart. Cardiomyopathy and Cardiovascular disease are some categories of heart diseases. The objective is to predict more accurately the presence of type of Heart Attack, the patient is more prone to have in future with reduced numbers of attributes along with suggestive treatment by using KAPPA rating. Originally, thirteen attributes namely Age, Sex, Chest Pain Type, Fasting Blood Sugar, Slope, CA, Exang, Serum Cholesterol, Oldpeak, Thalach, RestECG, Thal, and Trest-Blood Pressure was involved in predicting the heart disease. In this proposed work 13 attributes are reduced to 7 attributes such as Chest pain, Resting Blood Pressure, Exang, Resting - ECG, Thalach , Serum Cholesterol And Coronary Angiography. Three different algorithms namely Decision Tree (C4.5), Naive Bayes and Neural Network is used for Heart Attack Type prediction and then KAPPA rating is applied to output the most likely attack type. The Data-set used for Heart Attack consist analytical data. Total more than 500 records with 7 medical (symptoms) attributes are used for as input to system.