dc.contributor.author | Karaolis, Minas A. | en |
dc.contributor.author | Moutiris, Joseph Antoniou | en |
dc.contributor.author | Hadjipanayi, Demetra | en |
dc.contributor.author | Pattichis, Constantinos S. | en |
dc.creator | Karaolis, Minas A. | en |
dc.creator | Moutiris, Joseph Antoniou | en |
dc.creator | Hadjipanayi, Demetra | en |
dc.creator | Pattichis, Constantinos S. | en |
dc.date.accessioned | 2019-11-13T10:40:38Z | |
dc.date.available | 2019-11-13T10:40:38Z | |
dc.date.issued | 2010 | |
dc.identifier.issn | 1089-7771 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/54218 | |
dc.description.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 | en |
dc.description.abstract | and 2) after the event: modifiable-smoking after the event, systolic blood pressure, diastolic blood pressure, total cholesterol, highdensity 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 | en |
dc.description.abstract | 2) for PCI, family history, history of hypertension, and history of diabetes | en |
dc.description.abstract | 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. © 2006 IEEE. | en |
dc.source | IEEE Transactions on Information Technology in Biomedicine | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-77953146336&doi=10.1109%2fTITB.2009.2038906&partnerID=40&md5=814bd3fa536988e90d7fe988c95eef4d | |
dc.subject | methodology | en |
dc.subject | article | en |
dc.subject | Algorithms | en |
dc.subject | human | en |
dc.subject | Humans | en |
dc.subject | adult | en |
dc.subject | aged | en |
dc.subject | female | en |
dc.subject | algorithm | en |
dc.subject | male | en |
dc.subject | biological model | en |
dc.subject | risk factor | en |
dc.subject | coronary artery disease | en |
dc.subject | Coronary Disease | en |
dc.subject | Diagnosis | en |
dc.subject | middle aged | en |
dc.subject | Heart | en |
dc.subject | Risk factors | en |
dc.subject | Blood | en |
dc.subject | Myocardial infarction | en |
dc.subject | Blood pressure | en |
dc.subject | Medical computing | en |
dc.subject | Cardiology | en |
dc.subject | Data sets | en |
dc.subject | Aged, 80 and over | en |
dc.subject | Data mining | en |
dc.subject | Glucose | en |
dc.subject | Models, Cardiovascular | en |
dc.subject | Decision trees | en |
dc.subject | Handicapped persons | en |
dc.subject | Classification rules | en |
dc.subject | C4.5 decision tree algorithm | en |
dc.subject | Causes of death | en |
dc.subject | Coronary artery bypass graft | en |
dc.subject | Coronary heart disease | en |
dc.subject | Coronary heart disease (CHD) | en |
dc.subject | decision tree | en |
dc.subject | Developed countries | en |
dc.subject | High-density lipoproteins | en |
dc.subject | Low density lipoproteins | en |
dc.subject | Percutaneous coronary intervention | en |
dc.subject | Related risk | en |
dc.subject | Splitting criterion | en |
dc.subject | Systolic blood pressure | en |
dc.title | Assessment of the risk factors of coronary heart events based on data mining with decision trees | en |
dc.type | info:eu-repo/semantics/article | |
dc.identifier.doi | 10.1109/TITB.2009.2038906 | |
dc.description.volume | 14 | |
dc.description.issue | 3 | |
dc.description.startingpage | 559 | |
dc.description.endingpage | 566 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Cited By :63</p> | en |
dc.source.abbreviation | IEEE Trans.Inf.Technol.Biomed. | en |
dc.contributor.orcid | Pattichis, Constantinos S. [0000-0003-1271-8151] | |
dc.gnosis.orcid | 0000-0003-1271-8151 | |