Assessment of the risk of coronary heart event based on data mining

Abstract

Coronary heart disease (CHD) is a major cause of morbidity and mortality in the western world. Although significant progress has been made in the diagnosis and treatment of CHD, further investigation is still needed. The objective of this study was to develop a data mining system for the assessment of heart event related risk factors. The risk factors investigated were: i. clinical: sex, age, smoking, systolic blood pressure, family history for premature CHD, history of hypertension, and diabetes; and ii. biochemical: cholesterol, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 620 cases were collected from the Paphos district in Cyprus, most of them with more than one event. Data mining analysis was carried out using the C4.5 decision trees algorithms. The most important risk factors, as extracted from the classification rules analysis were: sex, age, smoking, blood pressure, and cholesterol. Most of these risk factors were also extracted by other investigators. It is anticipated that data mining could help in the identification of high and low risk subgroups of patients, a decisive factor for the selection of therapy, i.e. medical or surgical. However, further investigation with larger data sets is still needed.