Data mining approach for coronary artery disease screening

Coronary artery disease (CAD) is the major cause of mortality in the world. Although there is a significant level of advancement in medical science and technology, this disease still remains challenging to the common people. The aim of this study is to develop a computer assisted screening system that will help early detection of CAD and improved patient management with the limited resources in the developing countries. The present system is developed from an initial marked data set. Ten risk factors have been investigated for the risk stratification of CAD. Two decision tree models -ID3 and CART, have been applied for finding a preliminary set of rules from the annotated database. The extracted rules have been clinically validated by a group of cardiologists as per their medical experience and acumen in finding a final set of rule base. The dataset used for automatic generation of model consists of 500 subjects. The present screening system provides risk stratification for CAD based on easily available medical data and it produces rules that can be easily interpreted by the medical experts. The developed system is ready to clinically validate on a large dataset.

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