using combined descriptive and predictive methods of data mining for coronary artery disease prediction: a case study approach

Heart disease is one of the major causes of morbidity in the world. currently, large proportions of the healthcare data are not processed properly, and thus fail to be effectively used for decision-making purposes. the risk of heart disease may be predicted via investigation of heart disease risk factors coupled with data mining knowledge. this paper presents a model developed using the combined descriptive and predictive techniques of data mining that aims to aid specialists in the healthcare system to effectively predict patients with coronary artery disease (cad). in order to achieve this objective, some clustering and classification techniques are used. first, the number of clusters are determined using clustering indices. next, some types of decision tree methods and artificial neural network are applied to each cluster in order to predict the cad patients. the results obtained show that the c&rt decision tree method performs best on all the data used in this work with 0.074 error. the data used in this work is real, and was collected from a heart clinic database.