Association rule analysis for the assessment of the risk of coronary heart events

Abstract

Although significant progress has been made in the diagnosis and treatment of coronary heart disease (CHD), further investigation is still needed. The objective of this study was to develop a data mining system using association analysis based on the apriori algorithm for the assessment of heart event related risk factors. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 369 cases were collected from the Paphos CHD Survey, most of them with more than one event. The most important risk factors, as extracted from the association rule analysis were: sex (male), smoking, high density lipoprotein, glucose, family history, and history of hypertension. Most of these risk factors were also extracted by our group in a previous study using the C4.5 decision tree algorithms, and by other investigators. Further investigation with larger data sets is still needed to verify these findings.