A Data Mining Model to Predict Coronary Artery Disease using Cleveland CAD dataset
Notice: Undefined variable: link_article in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818
Notice: Undefined property: stdClass::$image in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818
Notice: Undefined variable: title in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 818
Notice: Undefined offset: 1 in /home/ijtc/public_html/plugins/content/bt_socialshare/bt_socialshare.php on line 820
Notice: Undefined variable: pdFileDate in /home/ijtc/public_html/components/com_phocadownload/views/file/tmpl/default.php on line 277
Presently, Heart Diseases Especially Coronary Artery Disease (CAD) is one of the major cause of the fatality. The death rate because of Heart Disease is increasing day by day as compared to another disease. Coronary Artery Diseases the most common heart disease that occurs because of the blockage of arteries in the heart. But it is a tough task to predict the possible complications of Coronary Artery Disease well in advance. Traditional methods to predict CAD are time-consuming and high technical invasive. Data mining techniques have been used in the healthcare system for diagnosis and prediction of different type of diseases while ensuring high accuracy. Data mining techniques are effective because of the capability to predict the hidden patterns in the medical data. Therefore, the use of data mining techniques in predicting the CAD seems promising. This Research paper provides insighton thedifferent type of CAD prediction techniques. In this research, we proposed a hybrid data mining model to predict CAD. We have proposed the data mining technique using feature selection and artificial neural network training model using MATLAB, and we have achieved an accuracy of about 96% which is comparably higher than most of the existing work.
Data mining, Coronary Artery Disease, Heart Disease, Artificial neural network, feature selection.