Determining the role of factors leading to metabolic syndrome in coronary disease based on data mining approaches

Growth of cardiovascular diseases and their effects on society and the high cost of imports, caused the medical community to pursue plans for further evaluation, prevention, early detection and effective treatment so valuable knowledge can be created by using data mining and knowledge discovery in cardiology centers that the discovered knowledge can improve the quality of service by medical center’s managers and It can also be used by doctors to treat and predict cardiovascular disease by their disease history. In this research, to improve the diagnosis of cardiac diseases a classification - feature selection approach has been proposed that were evaluated by using a data set of patient records of Amir-Al-Momenin hospital in Tehran. Our proposed method is selected by using the different ways of classification on existing data of healthy persons and patients to reach the most efficient way and highest accuracy. At the end the learned classifier are evaluated by a set of test specimen. Finally evaluation shows that the classification tree method C4.5 classification accuracy has earned the highest standard of accuracy rate as 95.8%.