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Intelligent Three-High Diseases Home Warning System Based on Fuzzy Theory

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Abstract

With the progress of medical technology and the declining birth and death rates year by year, the population structure of Taiwan presents the aging phenomenon, which increases the demand for long-term care. The elderly often have many chronic diseases, mainly the so-called “Three-High Diseases”, which refer to hypertension, hyperglycemia and hyperlipidemia, and are related closely to our daily routes, healthy diet, and psychology. In order to establish a home care system for the elderly, this research takes the Four-Whole-Care System as one of the main concepts. The Four-Whole-Care System includes four items, whole person, whole process, whole family, and whole team, which provides continuous, comprehensive, and adaptable long-term care service. However, the Four-Whole-Care System has numerous fuzzy and uncertain items, which require a clear standard scope be defined through the inference of the Fuzzy Theory, as a warning standard to warn and remind the elderly and their family members. This research establishes a home care system for the elderly suffering from the Three-High Diseases, and sets up App service software to send out warnings of the Three-High Diseases in combination with the Four-Whole-Care System. We apply fuzzy theory to define and express a clear warning standard scope and assist the elderly and their family members to receive warnings of the Three-High Diseases. Our system also provides appropriate improvement suggestions, in order to enhance the quality of life of the elderly and provide care service more efficiently.

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References

  1. Kaufmann, A. (1975). Introduction to the theory of fuzzy subset. New York: Academic press.

    Google Scholar 

  2. Adeli, A., & Neshat, M. (2010) A fuzzy expert system for heart disease diagnosis. Proceedings of the International Multi Conference of Engineers and Computer Scientists 2010 (Vol I), IMECS 2010, March 17–19, 2010, Hong Kong.

  3. American Association of Diabetes Educatiors (AADE) (2001) A core curriculum for diabetes education: Diabetes education and program management. Chicago, IL: American Association of Diabetes Educatiors, pp. 3–17.

  4. Appel, L. J., Champagne, C. M., Harsha, D. W., Cooper, L. S., Obarzanek, E., Elmer, P. J., et al. (2003). Effects of comprehensive lifestyle modification on blood pressure control: Main results of the PREMIER clinical trial. JAMA, 289, 2083–2093.

    Google Scholar 

  5. DeSpelder, L. A., & Strickland, A. L. (2005). Health care systems: Patients, staff, and institutions. In L. A. DeSpelder & A. L. Strickland (Eds.), The last dance: Encountering death and dying (7th ed., pp. 125–151). Boston: McGraw Hill.

    Google Scholar 

  6. Chang, E., Dillon, T., Calder, D. (2008) Human system interaction with confident computing. In: Proceedings of the Conference Human System Interaction, Krakow, Poland, May 25–27, 2008, pp. 1–11.

  7. Francis, J., & Netten, A. (2004). Raising the quality of home care: A research of service users’ view. Social Policy & Administration, 38(3), 290–305.

    Article  Google Scholar 

  8. Knight, S., & Tjassing, H. (1994). Health care moves to the home. World Health, 4, 7–12.

    Google Scholar 

  9. Zadeh, L.A. (1973). Outline of a new approach to the analysis of complex systems and decision process. IEEE Transactions on systems, Man, and Cybernetics, SMC-1, pp. 28–44.

    Article  MathSciNet  Google Scholar 

  10. Jaradat, M. A. K., & Al-Nimr, M. A. (2009). Fuzy logic controller deployed for indoor air quality control in natura ventilation environments. Journal of Electrical Engineering, 60, 12–17.

    Google Scholar 

  11. de Leon, C. F. M., Glass, T. A., & Berkman, L. F. (2002). Social engagement and disability in a community population of older adult. American Journal of Epidemiology, 157(7), 633–642.

    Article  Google Scholar 

  12. Kolb, P. (2014). Understanding aging and diversity: Theories and concepts. New York: Routledge.

    Google Scholar 

  13. Russell, B. (1923). Vagueness. The Australasian Journal of Psychology and Philosophy, 1, 84–92.

    Article  Google Scholar 

  14. Thome, B., Dykes, A. K., & Hallberg, I. R. (2003). Home care with regard to definition, care recipients, content and outcome, systematic literature review. Journal of Clinical Nursing, 12, 860–872.

    Article  Google Scholar 

  15. Walker, A. (1983). A caring community. In H. Glennerster (Ed.), The future of the welfare state (pp. 157–172). London: Heinemann Educational Books.

    Google Scholar 

  16. Woodruff, R. (2003). Palliative medicine: Evidence-based symptomatic and supportive care for patients with advanced cancer and AIDS (4th ed.). Oxford, UK: Oxford University Press.

    Google Scholar 

  17. Wang Z, Yang R, Wang L, Green RC, Dounis AI (2011) A fuzzy adaptive comfort temperature model with grey predictor for multi-agent control system of smart building. In: Proceedings of the Congress on Evolutionary Computation CEC (2011) pp. 728–735.

  18. Xiang, Z. Z., & Liu, J. (2003). Existent problems and disquisitive direction of super speed elevator. Control Engineering of China, 10, 29–32.

    Google Scholar 

  19. Lin, C. C., Chen, C. C., Lin, P. S., Lee, R. G., Huang, J. S., Tsai, T. H., et al. (2016). Development of home-based frailty detection device using wireless sensor networks. Journal of Medical and Biological Engineering, 36(2), 168–177.

    Article  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the research support of the Ministry of Science and Technology, Taiwan through Research Project MOST 105-2410-H-029-045.

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Authors and Affiliations

  1. Department Of Information Management, Tunghai University, No. 1727, Sec. 4, Taiwan Boulevard, Xitun District, Taichung, 40704, Taiwan ROC

    Tzu-Chiang Chiang & Chia-Hsiang Chen

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  1. Tzu-Chiang Chiang
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  2. Chia-Hsiang Chen
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Correspondence to Tzu-Chiang Chiang.

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Chiang, TC., Chen, CH. Intelligent Three-High Diseases Home Warning System Based on Fuzzy Theory. J. Med. Biol. Eng. 38, 889–904 (2018). https://doi.org/10.1007/s40846-017-0368-4

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  • DOI: https://doi.org/10.1007/s40846-017-0368-4

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