Observation-prevention framework of cardiac risk factors: An Indian study

Journal of Medical Imaging and Health Informatics

Volumn 2, Issue 2, 2012, Pages 102-113

  Satapathy, Sagarika ^a   Chattopadhyay, Subhagata ^a  

^a NATIONAL INSTITUTE OF SCIENCE AND TECHNOLOGY   (India)

Author keywords

Factors; Framework; Heart attack; India; Risk; Statistical analysis

Indexed keywords

EID: 84877282980     PISSN: 21567018     EISSN: 21567026     Source Type: Journal    
DOI: 10.1166/jmihi.2012.1071     Document Type: Article

References (40)

1. J. A. Ambrose and R. S. Barua, The pathophysiology of cigarette smoking and cardiovascular disease: An update. Journal of the American College of Cardiology 43, 1731 (2004).
2. T. W. Anderson and D. A. Darling, A test of goodness of fit. Journal of the American Statistical Association 49, 765 (1954).
3. G. D. Batty, M. J. Shipley, M. Kivimaki, M. Marmot, and G. D. Smith, Walking pace, leisure time physical activity, and resting heart rate in relation to disease-specific mortality in London: 40 years follow-up of the original Whitehall study. An update of our work with professor Jerry N. Morris (1910-2009). Annals of Epidemiology 20, 661 (2010).
4. R. W. Brause, Medical analysis and diagnosis by neural networks. Lecture Notes in Computer Science 2199, 1 (2001).
5. S. Chattopadhyay and D. K. Pratihar, Towards developing intelligent autonomous systems in psychiatry: Its present state and future possibilities. Intelligent Autonomous Systems 275, 143 (2010).
6. S. Chattopadhyay, S. Rakesh, L. P. W. Land, and U. R. Acharya, Studying infant mortality rate: A data mining approach. Health and Technology 1, 25 (2011).
7. L. J. Cronbach, Coefficient alpha and the internal structure of tests. Psychometrika 16, 297 (1951).
8. F. L. Duff, C. Muntean, M. Cuggia, and P. Mabo, Predicting survival causes after out of hospital cardiac arrest using data mining method, MEDINFO International Medical Informatics Association (IMIA), edited by M. A. Granen, IOS Press, Amsterdam (2004), pp. 1256-59.
9. K. Edwards, D. Griffiths, J. Morgan, R. Pitman, and C. von Ruhland, Can the choice of intermediate solvent or resin affect glomerular basement membrane thickness? Nephrol Dial Transplant 24, 400 (2009).
10. T. S. Ferguson, M. K. Tulloch-Reid, N. O. Younger, R. A. Wright-Pascoe, M. S. Boyne, S. R. McFarlane, D. K. Francis, and R. J. Wilks, Cardiovascular disease among patients attending a specialist diabetes clinic in Jamaica. Dove Press Journal: Research reports in Clinical Cardiology 2, 41 (2011).
11. 2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society 85, 87 (1922).
12. T. R. Frieden and P. A. Briss, We can reduce dietary sodium, save money, and save lives. Annals of Internal Medicine 152, 526 (2010).
13. U. Goldbourt, S. Yaari, and J. H. Medalie, Isolated low HDL cholesterol as a risk factor for coronary heart disease mortality: A 21-year follow-up of 8000 men. Arteriosclerosis, Thrombosis, and Vascular Biology 17, 107 (1997).
14. B. D. Horne, N. J. Camp, J. B. Muhlestein, and L. A. Cannon-Albright, Identification of excess clustering of coronary heart diseases among extended pedigrees in a genealogical population database. American Heart Journal 2nd edn. 152, 305 (2005).
15. Report, Govt. of India online web document: http://india.gov.in/citizen/ health/health.php?id=11
16. WHO India report online web document: http://www.whoindia.org/LinkFiles/ NMH-Resources-burden-cvd-mortality.pdf.
17. T. A. Jilani, H. Yasin, M. Yasin, and C. Ardil, Acute coronary syndrome prediction using data mining techniques-an application. World Academy of Science, Engineering and Technology 59, 474 (2009).
18. R. N. Lemaitre, D. S. Siscovick, T. E. Raghunathan, S. Weinmann, P. Arbogast, and D.-Y. Lin, Leisure-time physical activity and the risk of primary cardiac arrest. Archives of Internal Medicine 159, 686 (1999).
19. K. Mahmud, Natural hormone therapy for menopause. Gynecological Endocrinology 26, 81 (2010).
20. N. Natarajan and R. S. D. W. Banu, Design methodology of a controller to forecast the uncertain cardiac arrest using fuzzy logic approach. Journal of Convergence Information Technology 3, 46 (2008).
21. NHS UK, Thursday November 19 (2009), Web document URL: http://www.nhs.uk/news/2009/11November/Pages/Alcohol-and-heart-disease-risk. aspx [last accessed 30/01/2012].
22. I. Njolstad, E. Arnesen, and P. G. Lund-Larsen, Smoking, serum lipids, blood pressure, and sex differences in myocardial infarction. Circulation 93, 450 (1996).
23. J. Nunnally, Psychometric Theory, 2nd edn., McGraw-Hill, New York (1978).
24. N. I. Parikh, P. Gona, M. G. Larson, C. S. Fox, E. J. Benjamin, J. M. Murabito, C. J. O'Donnell, R. S. Vasan, and D. Levy, Long-term trends in myocardial infarction incidence and case fatality in the national heart, lung, and blood institute's Framingham heart study. Circulation 119, 1203 (2009).
25. M. Petretta, P. Costanzo, P. Perrone-Filardi, and M. Chiariello, Impact of gender in primary prevention of coronary heart disease with statin therapy: A meta-analysis. International Journal of Cardiology 138, 25 (2010).
26. E. Pitt, Application of data mining techniques in the prediction of coronary artery disease: Use of anaesthesia time-series and patient risk factor data, Master of Information Technology Thesis, Queensland University of Technology, eprints.qut.edu.au/34427/ (Last acessed-14.5.2011) (2009).
27. A. Rajkumar and G. S. Reena, Diagnosis of heart disease using data mining algorithm. Global Journal of Computer Science and Technology 10, 38 (2010).
28. A. Rozanski, H. Gransar, L. D. Kubzansky, N. Wong, L. Shaw, R. Miranda-Peats, L. E. Thomson, S. W. Hayes, J. D. Friedman, and D. S. Berman, Do psychological risk factors predict the presence of coronary atherosclerosis? Psychosomatic Medicine 73, 7 (2011).
29. S. Satapathy and S. Chattopadhyay, Mining important factors of heart attack, Proceedings of International Conference on Advances in Recent Technologies in Communication and Computing (ARTCOM-2011)., Curran Associates, Inc. Bangalore, India (2011), pp. 143-147.
30. R. See, J. B. Lindsay, M. J. Patel, C. R. Ayers, A. Kera, D. K. McGuire, S. M. Grundy, and J. A. de Lemos, Application of the screening for heart attack prevention and education task force recommendations to an urban population. Archives of Internal Medicine 168, 1055 (2008).
31. H. D. Sesso, M. J. Stampfer, B. Rosner, C. H. Hennekens, J. A. E. Manson, and J. M. Gaziano, Seven-year changes in alcohol consumption and subsequent risk of cardiovascular disease in men. Archives of Internal Medicine 160, 2605 (2000).
32. D. C. Sharma, India's no. 1 killer: Heart disease. India Today, April 12 http://indiatoday.intoday.in/story/India%27s + no. 1 + killer: + Heart + disease/1/92422.html [on line, last accessed 12.01.2012] (2010).
33. K. Srinivas, B. K. Rani, and A. Govrdhan, Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering 02, 250 (2010).
34. J. A. Swope, N. K. Kasabov, and M. J. A. Williams, Neuro-Fuzzy Modelling of Heart Rate Signals and Application to Diagnostics. In Fuzzy Systems in Medicine, edited by P. S. Szczepaniak, P. J. G. Lisboa, and J. Kacprzyk, Physica Verlag, Heidelberg (1999), pp. 519-542.
35. S. G. Wannamethee, A. G. Shaper, and M. Walker, Overweight and obesity and weight change in middle aged men: Impact on cardiovascular disease and diabetes. Journal of Epidemiology and Community Health 59, 134 (2005).
36. www.minitab.com.
37. E. Yargholy and S. Parvaneh, Evaluation of risk factors selection in cardiac risk stratification. Computers in Cardiology 35, 665 (2008).
38. E. Yargholi and S. Parvaneh, Novel Cardiac Risk Factor Stratification Using Neuro-fuzzy Tool, The proceedings of International Conference on Computational Intelligence for Modelling Control & Automation (2008), pp. 1199-1204.
39. R. W. Yeh, S. Sidney, M. Chandra, M. Sorel, J. V. Selby, and A. S. Go, Population trends in the incidence and outcomes of acute myocardial infarction. New England Journal of Medicine 362, 2155 (2010).
40. Z. Zhuo, Y.-J. Tsai, E. Ackerman, and L. Gatewood, Polychotomous multivariate models for coronary heart disease simulation. IV The impact of physiological aging. International Journal of Medical Informatics 37, 287 (1994).