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Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS–LDA)

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Abstract

Coronary heart disease is a great concern in the field of healthcare, and one of the main causes of death across the world. In the USA, as in Europe, it is responsible for the highest mortality rate. Although the risk of coronary heart disease has been recognized, few studies have been conducted on this topic. On the other hand, computer science has become an important part of our lives. The use of medicine and medical science-related artificial intelligence facilitating the diagnosis and analysis of diseases and health problems is attracting considerable attention. The present study focuses on the determination of the optimum method for using artificial intelligence in a clinical decision support system in order to provide a solution and diagnosis regarding the research and medical issues related to the application of such a system. In the present study, we have developed a prediction model capable of the risk assessment of coronary heart disease by optimizing an adaptive-network-based fuzzy inference system (ANFIS) and linear discriminant analysis (LDA) on the basis of the dataset of Korean National Health and Nutrition Examinations Survey V. The ANFIS–LDA method, which is optimized using a hybrid method, exhibits a high prediction rate of 80.2 % and is more efficient and effective than the existing methods. We expect that our study to contribute to the prevention of coronary heart disease.

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References

  1. Evans JA, Bethell HJN, Turner SC (2006) NSF for CHD: 3 years of 12-month follow-up audit after cardiac rehabilitation. J Public Health 28(1):35–38

    Article  Google Scholar 

  2. Health for life: Better health, better health care, American Heart Association. http://www.aha.org/aha/content/2007/pdf/071204_H4L_Efficient_Affordable.pdf. Accessed April 2010

  3. Kwak S (2013) Study on family strength and happiness of the pre-elderly and the elderly. J Korean Home Econ Assoc 51(1):1–16

    Google Scholar 

  4. The world health report 2008, World Health Organization. http://www.who.int/whr/2008/whr08_en.pdf. Accessed November 2010

  5. Wilson P, D’Agostino R, Levy D, Belanger A, Silbershatz H, Kannel W (1998) Prediction of coronary heart disease using risk factor categories. Circulation 97(18):1837–1874

    Google Scholar 

  6. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  MATH  MathSciNet  Google Scholar 

  7. Mohd Izani Mohamed R, Al-Anbuky A (2013) Wireless sensor networks and human comfort index. Pers Ubiquit Comput 17(5):999–1011. doi:10.1007/s007779-012-054-7-9

  8. Berry MJA, Linoff G (1997) Data mining techniques: for marketing, sales, and customer support. Wiley, New York

    Google Scholar 

  9. Cadavid S, Abdel-Mottaleb M, Helal A (2012) Exploiting visual quasi-periodicity for real-time chewing event detection using active appearance models and support vector machines. Pers Ubiquit Comput 16(6):729–739

    Article  Google Scholar 

  10. Muñoz A et al (2011) Design and evaluation of an ambient assisted living system based on an argumentative multi-agent system. Pers Ubiquit Comput 15(4):377–387

    Article  Google Scholar 

  11. Varkey JP, Pompili D, Walls TA (2012) Human motion recognition using a wireless sensor-based wearable system. Pers Ubiquit Comput 16(7):897–910

    Article  Google Scholar 

  12. Tang F, Tao H (2007) Fast linear discriminant analysis using binary bases. Pattern Recogn Lett 28(16):2209–2218

    Article  Google Scholar 

  13. Jung EY, Kim JH, Chung KY, Park DK (2013) Home health gateway based healthcare services through U-health platform. Wireless Pers Commun. doi:10.1007/s11277-013-1231-8

    Google Scholar 

  14. Kim GH, Kim YG, Chung KY (2013) Towards virtualized and automated software performance test architecture. Multimed Tools Appl. doi:10.1007/s11042-013-1536-3

    Google Scholar 

  15. Baek SJ, Han JS, Chung KY (2013) Dynamic reconfiguration based on goal-scenario by adaptation strategy. Wireless Pers Commun. doi:10.1007/s11277-013-1239-0

    Google Scholar 

  16. Kim JH, Chung KY (2013) Ontology-based healthcare context information model to implement ubiquitous environment. Multimed Tools Appl. doi:10.1007/s11042-011-0919-6

    Google Scholar 

  17. Kang SK, Chung KY, Lee JH (2013) Development of head detection and tracking systems for visual surveillance. Pers Ubiquit Comput 1–8. doi:10.1007/s00779-013-0668-9

  18. Korea Centers for Disease Control and Prevention (2010) 5th Korean National Health and Nutrition Examinations Survey (KNHANES V-1). Centers for Disease Control and Prevention

  19. Vahid K, Gholam AM (2010) A fuzzy-evidential hybrid inference engine for coronary heart disease risk assessment. Int J Expert Syst Appl 37(12):8536–8542

    Article  Google Scholar 

  20. Fidele B, Cheeneebash J, Gopaul A, Goorah S (2009) Artificial neural network as a clinical decision-supporting tool to predict cardiovascular disease. Trends Appl Sci Res 4(1):36–46

    Article  Google Scholar 

  21. Kim SH, Chung KY (2013) Medical information service system based on human 3D anatomical model. Multimed Tools Appl. doi:10.1007/s11042-013-1584-8

    Google Scholar 

  22. Karaolis MA et al (2010) Assessment of the risk factors of coronary heart events based on data mining with decision trees. IEEE Trans Inf Technol Biomed 14(3):559–566

    Article  Google Scholar 

  23. Liu H-C, et al. (2012) Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets 1–14

  24. Dogantekin E et al (2010) An intelligent diagnosis system for diabetes on linear discriminant analysis and adaptive network based fuzzy inference system: LDA-ANFIS. Digit Signal Process 20(4):1248–1255

    Article  MathSciNet  Google Scholar 

  25. Dogantekin E, Dogantekin A, Avci D (2009) Automatic hepatitis diagnosis system based on linear discriminant analysis and adaptive network based on fuzzy inference system. Int J Expert Syst Appl 36(8):11282–11286

    Article  Google Scholar 

  26. Tsipouras MG, Exarchos TP, Fotiadis DI, Kotsia AP, Vakalis KV, Naka KK, Michalis LK (2008) Automated diagnosis of coronary artery disease based on data mining and fuzzy modeling. IEEE Trans Inf Technol Biomed 12(4):447–458

    Article  Google Scholar 

  27. Abidin B, Dom R, Rashid A, Rahman A, Bakar R (2009) Use of fuzzy neural network to predict coronary heart disease in a Malaysian sample. In Proceedings of the 8th WSEAS international conference on telecommunications and informatics

  28. Conroy RM, Pyörälä K, De Backer G, De Bacquer D, Wilhelmsen L, Graham IM (2003) SCORE Project Group: estimation of ten year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J 24:987–1003

    Article  Google Scholar 

  29. Menotti A, Puddu PE, Lanti M (2000) Comparison of the Framingham risk function-based coronary chart with risk function from an Italian population study. Eur Heart J 21:365–370

    Article  Google Scholar 

  30. Kiyong N, Heon Gyu L, Keun Ho R (2007) Data mining approach for diagnosing heart disease. Korea Res Inst Stand Sci 10(2):147–154

    Google Scholar 

  31. Guazzelli A (2011) Predictive analytics in healthcare. IBM Corporation

  32. Bersini H, Bontempi G (1997) Now comes the time to defuzzify neuro-fuzzy models. Fuzzy Sets Syst 90(2):161–169

    Article  Google Scholar 

  33. Jang J-SR (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  34. Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7(2):179–188

    Article  Google Scholar 

  35. Zhu W, Zeng N, Wang N (2010) Sensitivity, specificity, accuracy, associated confidence interval and ROC analysis with practical SAS® implementations. In: NESUG proceedings: health care and life sciences. Baltimore, Maryland

  36. Anooj PK (2012) Clinical decision support system: Risk level prediction of heart disease using decision tree fuzzy rule. Int J Res Rev Comput Sci 3(3):1659–1667

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Industrial Strategic Technology Development Program, 10037283, funded by the Ministry of Trade, Industry & Energy (MI, Korea).

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Correspondence to Young-Ho Lee.

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Yang, JG., Kim, JK., Kang, UG. et al. Coronary heart disease optimization system on adaptive-network-based fuzzy inference system and linear discriminant analysis (ANFIS–LDA). Pers Ubiquit Comput 18, 1351–1362 (2014). https://doi.org/10.1007/s00779-013-0737-0

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