Assessment of the Risk Factors of Coronary Heart Events Based on Data Mining With Decision Trees

Abstract

Coronary heart disease (CHD) is one of the major causes of disability in adults as well as one of the main causes of death in the developed countries. 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 targeting in the reduction of CHD events. The risk factors investigated were: 1) before the event: a) nonmodifiable-age, sex, and family history for premature CHD, b) modifiable-smoking before the event, history of hypertension, and history of diabetes; and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, and glucose. The events investigated were: myocardial infarction (MI), percutaneous coronary intervention (PCI), and coronary artery bypass graft surgery (CABG). A total of 528 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 tree algorithm for the aforementioned three events using five different splitting criteria. The most important risk factors, as extracted from the classification rules analysis were: 1) for MI, age, smoking, and history of hypertension; 2) for PCI, family history, history of hypertension, and history of diabetes; and 3) for CABG, age, history of hypertension, and smoking. Most of these risk factors were also extracted by other investigators. The highest percentages of correct classifications achieved were 66%, 75%, and 75% for the MI, PCI, and CABG models, respectively. It is anticipated that data mining could help in the identification of high and low risk subgroups of subjects, a decisive factor for the selection of therapy, i.e., medical or surgical. However, further investigation with larger datasets is still needed.

An empirical study of Bayesian network parameter learning with monotonic influence constraints

“…Here, a well learnt BN is important because rapid and accurate identification of hidden risk factors and conditions modelled by the network are important to support a doctors' decision making about treatments which reduce mortality rate [31]. In this experiment, we elicited monotonic influences from medical experts and were also give access to a hospital dataset.…”

A Data Mining Model to predict and analyze the events related to Coronary Heart Disease using Decision Trees with Particle Swarm Optimization for Feature Selection

“…Control group subjects had significantly less arterial stiffness (as reflected by both a lower PWV and a lower Augmentation Index). Not significant but still apparent difference between pulse wave velocity in peripheral arteries in patients with CHD compared with control group indicates that changes of elastic properties of the walls of peripheral arteries are less obvious or peripheral Karaolis et al [6] developed a data mining system for the assessment of heart related risk factors using Association analysis based on Apriori algorithm. A total of 369 cases have been collected from a hospital and three sorts of events have been investigated such as Myocardial Infarction (MI), Percutaneous Coronary Intervention (PCI) and Coronary Artery Graft Bypass Surgery (CABG).…”

A fuzzy analytic hierarchy attribute weighting and deep learning for improving CHD prediction of optimized semi parametric extended dynamic bayesian network

“…So that it is necessary for people to go for a heart disease diagnosis. Coronary Heart Disease (CHD) [2] is a major cause of disability in adults. CHD includes hyperlipidemia, myocardial infarction, and angina pectoris.…”