Analytical study of heart disease diagnosis using classification techniques

Heart disease is the number one problem for world. Heart disease more than people deaths occur during the first heart attack. But not only for heart attack have some problems attacked for breast cancer, lung cancer, ventricle. Valve, etc... It is essential to have a frame work that can effectually recognize the prevalence of heart disease in thousands of samples instaneously. In this paper the potential of nine (9) classification techniques was evaluated of prediction of heart disease. Namely decision tree, naive Bayesian neural network, SVM.ANN, KNN. My proposed algorithm of Apriori algorithm and SVM (support vector machine) in heart disease prediction. Using medical profiles such as a age, sex, blood pressure, chest pain type, fasting blood sugar. It can predict like of patients getting heart disease Based on this, medical society takes part interest in detecting and preventing the heart disease. From the analysis it have proved that classification based techniques contribute high effectiveness and obtain high accuracy compare than the previous methods.