Advanced Coherent System For Predicting Cardiac Risks using Data Mining Techniques

Abstract

Considering health care and medical industry related data there are millions or tons of data which contains numerous hidden information. This information can be mined through which we can make effective decisions in their related industry. There are numerous far advanced methods and techniques in mining and determining the useful decisions using the retrieved useful information. Such an effective system called Coherent cardiac risk prediction system (CCRPS) is developed using neural networks in early detection or prediction of various risk level in cardiac disease. This work employs a multilayer perception neural network with back propagation as the training algorithm. This system aims in predicting the likelihood of patients getting disease related to cardiac such as CHD, a prior heart attack, uncontrolled hypertension, abnormal heart valves, congenital heart disease (heart defects present at birth) and heart muscle disease. The system uses a total of twenty-one medical related parameters such as age, sex, chest pain type, resting blood pressure (in mm Hg on admission to the hospital), serum cholesterol in mg/dl, Smoking, stress etc for prediction purpose. It enables or activated the important knowledge such as how the medical factors related to cardiac disease and patterns and the relationship to be established. Through this system we obtain effective results that have crafted its own diagnostic method or way to predict the risk level measurement of cardiac disease.