An Analytical Approach to Predict the Cardio Vascular Disorder

Abstract

In recent times, heart disease has been recognized as the world's leading cause of death. However, it is also regarded as the disease that is most easily controlled and prevented. Recently, World Health Organization (WHO) claims that heart disease's progression and associated treatment expenses can both be significantly halted with the help of an early and prompt diagnosis. Therefore, researchers have employed various data mining approaches to diagnose heart disease in consideration of the rising number of deaths caused by the disease. This research study applied data mining classification modeling techniques, specifically discriminant analysis on the heart disease dataset for the prediction of chances of heart disease based on various attributes and assess the contribution of each attribute towards the heart disease. Lastly, the range and the accuracy of the classification are assessed. This dataset has an accuracy of 85.3% in predicting that whether individual has heart disease or not and the specificity of individual possess heart disease is 84.8% while normal individuals acquire specificity of 85.9%.