Heart Disease Prediction Using Classification Techniques with Feature Selection Method

Heart disease is now turned out most deadly disease throughout the world. Due to misdiagnosis of heart disease more people losing their lives. Hence, there is a need of automate the system for correct diagnose the heart disease based on the historical data. To aid early and correct diagnose of heart disease, many data mining techniques are used to predicting the disease. The high volume of medical data offered data mining techniques to discover the hidden pattern. Classification technique is one among the data mining techniques predict the heart disease. This paper presents, classification techniques applied for prediction of heart disease in two scenario such as dataset with all 13 attributes and 6 attributes selected by attribute selection method. For achieving the results, the selected classification techniques are Support vector machine, Neural Network (Multilayer perception), Bagging, Classification via regression and Simple logistic. And correlates the accuracy and time taken to build the prediction model for all used classification techniques in two different scenario.