Assessment of the risk factors of coronary heart events based on data mining with decision trees

IEEE Transactions on Information Technology in Biomedicine

Volumn 14, Issue 3, 2010, Pages 559-566

  Karaolis, Minas A ^a   Moutiris, Joseph A ^b   Hadjipanayi, Demetra ^a   Pattichis, Constantinos S ^a  

^a UNIVERSITY OF CYPRUS   (Cyprus)

^b Paphos General Hospital   (Cyprus)

Author keywords

Coronary heart disease (CHD); Data mining; Decision trees; Risk factors

Indexed keywords

C4.5 DECISION TREE ALGORITHM; CAUSES OF DEATH; CLASSIFICATION RULES; CORONARY ARTERY BYPASS GRAFT; CORONARY HEART DISEASE; CORONARY HEART DISEASE (CHD); DATA SETS; DEVELOPED COUNTRIES; HIGH-DENSITY LIPOPROTEINS; LOW DENSITY LIPOPROTEINS; MYOCARDIAL INFARCTION; PERCUTANEOUS CORONARY INTERVENTION; RELATED RISK; RISK FACTORS; SPLITTING CRITERION; SYSTOLIC BLOOD PRESSURE;

BLOOD; BLOOD PRESSURE; CARDIOLOGY; DATA MINING; DECISION TREES; DIAGNOSIS; GLUCOSE; HANDICAPPED PERSONS; HEART;

MEDICAL COMPUTING;

ADULT; AGED; ALGORITHM; ARTICLE; BIOLOGICAL MODEL; CORONARY ARTERY DISEASE; DATA MINING; DECISION TREE; FEMALE; HUMAN; MALE; METHODOLOGY; MIDDLE AGED; RISK FACTOR;

ADULT; AGED; AGED, 80 AND OVER; ALGORITHMS; CORONARY DISEASE; DATA MINING; DECISION TREES; FEMALE; HUMANS; MALE; MIDDLE AGED; MODELS, CARDIOVASCULAR; RISK FACTORS;

EID: 77953146336     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2009.2038906     Document Type: Article

References (36)

1. British Heart Foundation. (2008, Mar. 8). European Cardiovascular Disease Statistics. [Online]. Available: http://www. heartstats.org/datapage.asp?id= 7683
2. Euroaspire study group, "A European Society of Cardiology survey of secondary prevention of coronary heart disease: Principal results," Eur. Heart J., vol.18, pp. 1569-1582, 1997.
3. Euroaspire II Study Group, "Lifestyle and risk factor management and use of drug therapies in coronary patients from 15 countries," Eur. Heart J., vol.22, pp. 554-572, 2002.
4. Euroaspire stusy group, "Euroaspire III: A survey on the lifestyle, risk factors and use of cardioprotective drug therapies in coronary patients from 22 European countries," Eur. J. Cardiovasc. Prev. Rehabil., vol.16, no.2, pp. 121-137, 2009.
5. T. Marshall, "Identification of patients for clinical risk assessment by prediction of cardiovascular risk using default risk factor values," Br. Med. Assoc. Public Health, vol.8, p. 25, 2008.
6. W. B. Kannel, "Contributions of the Framingham Study to the conquest of coronary artery disease," Amer. J. Cardiol., vol.62, pp. 1109-1112, 1988.
7. M. Karaolis, J. A.Moutiris, and C. S. Pattichis, "Assessment of the risk of coronary heart event based on data mining," in Proc. 8th IEEE Int. Conf. Bioinformatics Bioeng., 2008, pp. 1-5.
8. Z. Wang and W. E. Hoy, "Is the Framingham coronary heart disease absolute risk function applicable to Aboriginal people?" Med. J. Australia, vol.182, no.2, pp. 66-69, 2005.
9. P. Brindle, J. Emberson, F. Lampe, M.Walker, P.Whincup, T. Fahey, and S. Ebrahim, "Predictive accuracy of the Framingham coronary risk score in British men: Prospective cohort study," Br. Med. Assoc., vol.327, pp. 1267-1270, 2003.
10. S. Sheridan, M. Pignone, and C. Mulrow, "Framingham-based tools to calculate the global risk of coronary heart disease: A systematic review of tools for clinicians," J. Gen. Intern. Med., vol.18, no.12, pp. 1060-1061, 2003.
11. T. A. Pearson, S. N. Blair, S. R. Daniels, R. H. Eckel, J. M. Fair, S. P. Fortmann, B. A. Franklin, L. B. Goldstein, Ph. Greenland, S. M. Grundy, Y. Hong, N. H. Miller, R.M. Lauer, I. S. Ockene, R. L. Sacco, J. F. Sallis, S. C. Smith, N. J. Stone, and K. A. Taubert, "AHA guidelines for primary prevention of cardiovascular disease and stroke," Circulation, vol.106, no.3, pp. 388-391, 2002.
12. S. M. Grundy, R. Pasternak, P. Greenland, S. Smith, and V. Fuster, "Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations," Amer. Heart Assoc., vol.100, pp. 1481-1492, 1999.
13. V. Podgorelec, P. Kokol, B. Stiglic, and I. Rozman, "Decision trees: An overview and their use in medicine," J. Med. Syst., vol.26, no.5, pp. 445- 463, 2002.
14. C. Ordonez, "Comparing association rules and decision trees for disease prediction," in Proc. Int. Conf. Inf. Knowl. Manage.,Workshop Healthcare Inf. Knowl. Manage. Arlington, VA, 2006, pp. 17-24.
15. C. Ordonez, E. Omiecinski, L. de Braal, C. A. Santana, N. Ezquerra, J. A. Taboada, D. Cooke, E. Krawczvnska, and E. V. Garcia, "Mining constrained association rules to predict heart disease," in Proc. IEEE Int. Conf. Data Mining (ICDM 2001), pp. 431-440.
16. D. Gamberger and R. Boškovíc Institute, Zarageb, Croatia, "Medical prevention: Targeting high-risk groups for coronary heart disease," Sol-EU-Net: Data Mining Decision Support [Online]. Available: http://soleunet.ijs.si/website/other/case-solutions/CHD.pdf.
17. C. L. Tsien, H. S. F. Fraser, W. J. Long, and R. L. Kennedy, "Using classification trees and logistic regression methods to diagnosemyocardial infraction," in Proc. 9th World Congr. Med. Inf., vol.52, pp. 493-497, 1998.
18. R. B. Rao, S. Krishan, and R. S. Niculescu, "Data mining for improved cardiac care," ACM SIGKDD Explorations Newslett., vol.8, no.1, pp. 3- 10, 2006.
19. J. Zavrsnik, P. Kokol, I. Maleiae, K. Kancler, M. Mernik, and M. Bigec, "ROSE: Decision trees, automatic learning and their applications in cardiac medicine," Medinfo, vol.8, no.2, p. 1688, 1995.
20. K. Polat, S. Sahan, H. Kodaz, and S. Guenes, "A hybrid approach to medical decision support systems: combining feature selection, fuzzy weighted pre-processing and AIRS," Comput.Methods Programs Biomed., vol.88, no.2, pp. 164-174, 2007. (Pubitemid 47588443)
21. S. A. Pavlopoulos, A. Ch. Stasis, and E. N. Loukis, "A decision treebased method for the differential diagnosis of aortic stenosis from mitral regurgitation using heart sounds," Biomed. Eng. OnLine, vol.3, p. 21, 2004.
22. C. A. Pena-Reyes, "Evolutionary fuzzy modeling human diagnostic decisions," Ann. NY Acad. Sci., vol.1020, pp. 190-211, 2004.
23. K. Boegl, K.-P. Adlassnig, Y. Hayashi, T. E. Rothenfluh, and H. Leitich, "Knowledge acquisition in the fuzzy knowledge representation framework of amedical consultation system," Artif. Intell. Med., vol.30, no.1, pp. 1- 26, 2004.
24. D. Michie, D. J. Spiegelhalter, and C. C. Taylor, Machine Learning, Neural and Statistical Classification. West Susser, England: Ellis Horwood, 1994.
25. J. R. Quinlan, in C4.5 Programs for Machine Learning, C. Schaffer, Ed. San Mateo, CA: Morgan Kaufmann, 1993.
26. J. Han and M. Kamber, Data Mining, Concepts and Techniques, 2nd ed. San Francisco, CA: Morgan Kaufmann, 2001.
27. J. R. Quinlan, "Simplifying decision trees," Int. J. Man-Mach. Stud., vol.27, pp. 221-234, 1987.
28. L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and Regression Trees. Belmont, CA: Wadsworth Int. Group, 1984.
29. F. Attneave, Applications of Information Theory to Psychology. New York: Holt, Rinehart, and Winston, 1959.
30. R. Lopez de Mantras, "A distance-based attribute selection measure for decision tree induction," Mach. Learn., vol.6, pp. 81-92, 1991.
31. T. Niblett, "Constructing Decision trees in noisy domains," in Proc. 2nd Eur. Working Session Learn., 1987, pp. 67-78.
32. L. Rokach and O.Maimon, Data Mining with Decision Trees, Theory and Applications. Singapore: World Scientific, 2008.
33. F. Wilcoxon, "Individual comparisons by ranking methods," Biometrics, vol.1, pp. 80-83, 1945.
34. P.-N. Tan, Introduction toDataMining. Reading, MA:Addison-Wesley, 2006.
35. T. D. Rea, S. R. Heckbert, R. C. Kaplan, N. L. Smith, R. N. Lemaitre, and B. M. Psaty, "Smoking status and risk for recurrent coronary events after myocardial infraction," Ann. Int. Med., vol.137, pp. 494-500, 2002.
36. M. Karaolis, J. A. Moutiris, L. Papaconstantinou, and C. S. Pattichis, "Association rule analysis for the assessment of the risk of coronary heart events," in Proc. 31st Annu. Int. IEEE Eng. Med. Biol. Soc. Conf., Minneapolis, MN, Sep. 2-6, 2009, pp. 6238-6241.