Table of Contents Author Guidelines Submit a Manuscript
Journal of Healthcare Engineering
Volume 2017, Article ID 2780501, 13 pages
https://doi.org/10.1155/2017/2780501
Research Article

Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

Department of Computer Engineering, Inha University, Incheon, Republic of Korea

Correspondence should be addressed to Sanggil Kang; rk.ca.ahni@gnakgs

Received 10 April 2017; Revised 5 July 2017; Accepted 12 July 2017; Published 6 September 2017

Academic Editor: Eddie Ng Yin Kwee

Copyright © 2017 Jae Kwon Kim and Sanggil Kang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Cardiovascular Diseases (CVDs) Fact Sheet N°317, WHO, 2015, [updated May 2017]. http://www.who.int/mediacentre/factsheets/fs317/en/index.html.
  2. Y. Maneerat, K. Prasongsukarn, S. Benjathummarak, W. Dechkhajorn, and U. Chaisri, “Intersected genes in hyperlipidemia and coronary bypass patients: feasible biomarkers for coronary heart disease,” Atherosclerosis, vol. 252, pp. e183–e184, 2016. View at Google Scholar
  3. T. Nakashima, T. Noguchi, S. Haruta et al., “Prognostic impact of spontaneous coronary artery dissection in young female patients with acute myocardial infarction: a report from the angina pectoris–myocardial infarction multicenter investigators in Japan,” International Journal of Cardiology, vol. 207, pp. 341–348, 2016. View at Publisher · View at Google Scholar · View at Scopus
  4. J. S. Zebrack, J. L. Anderson, C. A. Maycock et al., “Usefulness of high-sensitivity C-reactive protein in predicting long-term risk of death or acute myocardial infarction in patients with unstable or stable angina pectoris or acute myocardial infarction,” The American Journal of Cardiology, vol. 89, no. 2, pp. 145–149, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. W. B. Kannel, T. Gordon, W. P. Castelli, and J. R. Margolis, “Electrocardiographic left ventricular hypertrophy and risk of coronary heart disease. The Framingham study,” Annals of Internal Medicine, vol. 72, no. 6, pp. 813–822, 1970. View at Publisher · View at Google Scholar
  6. S. Cook, E. Ladich, G. Nakazawa et al., “Correlation of intravascular ultrasound findings with histopathological analysis of thrombus aspirates in patients with very late drug-eluting stent thrombosis,” Circulation, vol. 120, no. 5, pp. 391–399, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. S. E. Nissen, E. M. Tuzcu, P. Libby et al., “Effect of antihypertensive agents on cardiovascular events in patients with coronary disease and normal blood pressure: the CAMELOT study: a randomized controlled trial,” JAMA, vol. 292, no. 18, pp. 2217–2225, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. R. O. Bonow, B. A. Carabello, K. Chatterjee et al., “2008 focused update incorporated into the ACC/AHA 2006 guidelines for the management of patients with valvular heart disease: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (writing committee to revise the 1998 guidelines for the management of patients with valvular heart disease) endorsed by the Society of Cardiovascular Anesthesiologists, Society for Cardiovascular Angiography and Interventions, and Society of Thoracic Surgeons,” Journal of the American College of Cardiology, vol. 52, no. 13, pp. e1–e142, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. R. Narain, S. Saxena, and A. K. Goyal, “Cardiovascular risk prediction: a comparative study of Framingham and quantum neural network based approach,” Patient Preference and Adherence, vol. 10, pp. 1259–1270, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Wu, W. Peters, and M. W. Morgan, “The next generation of clinical decision support: linking evidence to best practice,” Journal of Healthcare Information Management, vol. 16, no. 4, 50 pages, 2002. View at Google Scholar
  11. U. R. Acharya, O. Faust, N. A. Kadri, J. S. Suri, and W. Yu, “Automated identification of normal and diabetes heart rate signals using nonlinear measures,” Computers in Biology and Medicine, vol. 43, no. 10, pp. 1523–1529, 2013. View at Publisher · View at Google Scholar · View at Scopus
  12. C. Barbieri, F. Mari, A. Stopper et al., “A new machine learning approach for predicting the response to anemia treatment in a large cohort of end stage renal disease patients undergoing dialysis,” Computers in Biology and Medicine, vol. 61, pp. 56–61, 2015. View at Publisher · View at Google Scholar · View at Scopus
  13. B. Robson and S. Boray, “Implementation of a web based universal exchange and inference language for medicine: sparse data, probabilities and inference in data mining of clinical data repositories,” Computers in Biology and Medicine, vol. 66, pp. 82–102, 2015. View at Publisher · View at Google Scholar · View at Scopus
  14. S. A. I. Shenas, B. Raahemi, M. H. Tekieh, and C. Kuziemsky, “Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes,” Computers in Biology and Medicine, vol. 53, pp. 9–18, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. J. K. Kim, J. S. Lee, D. K. Park, Y. S. Lim, Y. H. Lee, and E. Y. Jung, “Adaptive mining prediction model for content recommendation to coronary heart disease patients,” Cluster Computing, vol. 17, no. 3, pp. 881–891, 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Verma, S. Srivastava, and P. C. Negi, “A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data,” Journal of Medical Systems, vol. 40, no. 7, pp. 1–7, 2016. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Zhao and C. Ma, “An intelligent system for noninvasive diagnosis of coronary artery disease with EMD-TEO and BP neural network,” in 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing, vol. 2, pp. 631–635, Shanghai, China, 2008. View at Publisher · View at Google Scholar · View at Scopus
  18. M. Akay, “Noninvasive diagnosis of coronary artery disease using a neural network algorithm,” Biological Cybernetics, vol. 67, no. 4, pp. 361–367, 1992. View at Publisher · View at Google Scholar · View at Scopus
  19. M. Kukar, I. Kononenko, C. Grošelj, K. Kralj, and J. Fettich, “Analysing and improving the diagnosis of ischaemic heart disease with machine learning,” Artificial Intelligence in Medicine, vol. 16, no. 1, pp. 25–50, 1999. View at Publisher · View at Google Scholar · View at Scopus
  20. R. Detrano, A. Janosi, W. Steinbrunn et al., “International application of a new probability algorithm for the diagnosis of coronary artery disease,” The American Journal of Cardiology, vol. 64, no. 5, pp. 304–310, 1989. View at Publisher · View at Google Scholar · View at Scopus
  21. P. N. Tan, Introduction to Data Mining, Pearson Addison Wesley, San Francisco, 2006.
  22. I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining: Practical Machine Learning Tools and Techniques, Morgan Kaufmann, USA, 2016.
  23. R. Chadha, S. Mayank, A. Vardhan, and T. Pradhan, “Application of data mining techniques on heart disease prediction: a survey,” in Emerging Research in Computing, Information, Communication and Applications, pp. 413–426, Springer India, 2016.
  24. H. M. R. Ugalde, J. C. Carmona, J. Reyes-Reyes, V. M. Alvarado, and J. Mantilla, “Computational cost improvement of neural network models in black box nonlinear system identification,” Neurocomputing, vol. 166, pp. 96–108, 2015. View at Publisher · View at Google Scholar · View at Scopus
  25. A. Shrikumar, P. Greenside, A. Shcherbina, and A. Kundaje, “Not Just a Black box: Learning Important Features through Propagating Activation Differences,” 2016, http://arxiv.org/abs/1605.01713. View at Google Scholar
  26. D. Sussillo and O. Barak, “Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks,” Neural Computation, vol. 25, no. 3, pp. 626–649, 2013. View at Publisher · View at Google Scholar · View at Scopus
  27. Korea Center for Disease Control and Prevention, The Six Korea National Health & Nutrition Examination Survey 2013 (KNHANES VI), January 2017, http://knhanes.cdc.go.kr/.
  28. A. G. Mainous, R. J. Koopman, V. A. Diaz, C. J. Everett, P. W. Wilson, and B. C. Tilley, “A coronary heart disease risk score based on patient-reported information,” The American Journal of Cardiology, vol. 99, no. 9, pp. 1236–1241, 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. S. Capewell, E. S. Ford, J. B. Croft, J. A. Critchley, K. J. Greenlund, and D. R. Labarthe, “Cardiovascular risk factor trends and potential for reducing coronary heart disease mortality in the United States of America,” Bulletin of the World Health Organization, vol. 88, no. 2, pp. 120–130, 2010. View at Publisher · View at Google Scholar · View at Scopus
  30. A. Field, Discovering statistics using IBM SPSS statstics, Sage, Washington DC, 2013.
  31. J. A. Swets, Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers, Psychology Press, New York, 2014.
  32. S. V. Stehman, “Selecting and interpreting measures of thematic classification accuracy,” Remote Sensing of Environment, vol. 62, no. 1, pp. 77–89, 1997. View at Publisher · View at Google Scholar · View at Scopus
  33. R. H. Fletcher, S. W. Fletcher, and G. S. Fletcher, Clinical Epidemiology: The Essentials, Lippincott Williams & Wilkins, USA, 2012.
  34. ISO, Guide to the Expression of Uncertainty in Measurement, International Organization for Standardization, Geneva, Switzerland, 1993.
  35. Z. Arabasadi, R. Alizadehsani, M. Roshanzamir, H. Moosaei, and A. A. Yarifard, “Computer aided decision making for heart disease detection using hybrid neural network-genetic algorithm,” Computer Methods and Programs in Biomedicine, vol. 141, pp. 19–26, 2017. View at Publisher · View at Google Scholar
  36. Q. K. Al-Shayea, “Artificial neural networks in medical diagnosis,” International Journal of Computer Science Issues, vol. 8, no. 2, pp. 150–154, 2011. View at Publisher · View at Google Scholar · View at Scopus
  37. W. G. Baxt, “Application of artificial neural networks to clinical medicine,” The Lancet, vol. 346, no. 8983, pp. 1135–1138, 1995. View at Publisher · View at Google Scholar · View at Scopus
  38. D. M. Lloyd-Jones, M. G. Larson, A. Beiser, and D. Levy, “Lifetime risk of developing coronary heart disease,” The Lancet, vol. 353, no. 9147, pp. 89–92, 1999. View at Publisher · View at Google Scholar · View at Scopus
  39. M. J. Legato, E. Padus, and E. D. Slaughter, “Women’s perceptions of their general health, with special reference to their risk of coronary artery disease: results of a national telephone survey,” Journal of Women's Health, vol. 6, no. 2, pp. 189–198, 1997. View at Publisher · View at Google Scholar
  40. A. Dudina, M. T. Cooney, D. D. Bacquer et al., “Relationships between body mass index, cardiovascular mortality, and risk factors: a report from the SCORE investigators,” European Journal of Cardiovascular Prevention & Rehabilitation, vol. 18, no. 5, pp. 731–742, 2011. View at Publisher · View at Google Scholar · View at Scopus
  41. M. Ezzati, S. Vander Hoorn, A. Rodgers et al., “Estimates of global and regional potential health gains from reducing multiple major risk factors,” The Lancet, vol. 362, no. 9380, pp. 271–280, 2003. View at Publisher · View at Google Scholar · View at Scopus
  42. G. Assmann, H. Schulte, A. von Eckardstein, and Y. Huang, “High-density lipoprotein cholesterol as a predictor of coronary heart disease risk. The PROCAM experience and pathophysiological implications for reverse cholesterol transport,” Atherosclerosis, vol. 124, pp. S11–S20, 1996. View at Publisher · View at Google Scholar · View at Scopus
  43. S. MacMahon, R. Peto, R. Collins, J. Godwin, J. Cutler, P. Sorlie et al., “Blood pressure, stroke, and coronary heart disease: part 1, prolonged differences in blood pressure: prospective observational studies corrected for the regression dilution bias,” The Lancet, vol. 335, no. 8692, pp. 765–774, 1990. View at Publisher · View at Google Scholar · View at Scopus
  44. W. B. Kannel, “Blood pressure as a cardiovascular risk factor: prevention and treatment,” JAMA, vol. 275, no. 20, pp. 1571–1576, 1996. View at Google Scholar
  45. W. C. Willett, A. Green, M. J. Stampfer et al., “Relative and absolute excess risks of coronary heart disease among women who smoke cigarettes,” New England Journal of Medicine, vol. 317, no. 21, pp. 1303–1309, 1987. View at Publisher · View at Google Scholar
  46. E. L. Barrett-Connor, B. A. Cohn, D. L. Wingard, and S. L. Edelstein, “Why is diabetes mellitus a stronger risk factor for fatal ischemic heart disease in women than in men? The Rancho Bernardo Study,” JAMA, vol. 265, no. 5, pp. 627–631, 1991. View at Google Scholar
  47. R. C. Sole, C. T. Lucas, L. U. Rivera, D. C. Salazar, and M. A. Saldaña, “[pp. 29.15] Obesity increases the central systolic and diastolic blood pressure despite having proper treatment in hypertensive patients,” Journal of Hypertension, vol. 34, article e303, 2016. View at Google Scholar
  48. Y. Aoki, S. S. Yoon, Y. Chong, and M. D. Carroll, “Hypertension, abnormal cholesterol, and high body mass index among non-Hispanic Asian adults: United States, 2011-2012,” NCHS Data Brief, vol. 140, pp. 1–8, 2014. View at Google Scholar