Prediction of heart disease severity with hybrid data mining

Nowadays, heart diseases are very common and one of the major causes of death across the globe. This calls for an accurate and timely diagnosis of the heart disease. There is abundant data available with the health care systems; however, the knowledge about the data is rather poor. The accessibility of the enormous size of medical dataset hints towards the requirement of a tool which analyses data to extract valuable information. Data scientists have attempted several methods in order to improvise the examination of large data sets. Previously, various data mining techniques have been implemented in the healthcare systems, however, the hybridization in addition to a single technique in the identification of heart disease shows promising outcomes, and can be useful in further investigating the treatment of the heart diseases. This work attempts to survey some recent techniques applied towards knowledge discovery for heart disease prediction and further proposes a novel prediction method with improved accuracy.