Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Design and Implementation of Chronic Disease Risk Analysis System according to Personalized Food Intake Preferences

개인 식품섭취 선호도에 따른 만성질환 발생 위험도 분석 시스템 설계 및 구현

  • Jeon, So Hye (Graduate Program of Biomedical Engineering, Graduate School, Yonsei University) ;
  • Kim, Nam Hyun (Department of Medical Enginnering, College of Medicine, Yonsei University)
  • 전소혜 (연세대학교 대학원 생체공학협동과정) ;
  • 김남현 (연세대학교 의과대학 의학공학교실)
  • Received : 2013.12.03
  • Published : 2014.03.25
Download PDF
⟨ Previous Next ⟩

Abstract

While variety of content on the internet has increased with the development of IT and person's needs about suitable information are increasing rapidly, studies for personalized service have been actively performed. In the study, we proposed the Hypertension and Diabetes risk analysis system according to personal food intake preference using the analysis method of buying preferences in product recommendation system. For the analysis of food intake preference, the Pearson correlation coefficient is used to calculate similarity weights between each reference analysis data and sample data and then reference data should be grouping into the similarity weights and calculating risk of hypertension and diabetes each group. To evaluate the significance of this system, 1,021 subjects are applied the system. Hypertension and diabetes groups' risk is significant higher than normal group statistically so, it is confirmed that food intake preference and the diseases were relevant. In this paper, we verify the validity of hypertension and diabetes risk analysis system using a personal food intake preference.

IT 기술의 발달로 인터넷에 다양한 콘텐츠가 늘고, 다양한 정보에서 개인의 적합한 정보를 제공받고자 하는 요구가 급증하면서 다양한 개인화 서비스를 제공 방법에 관한 연구가 활발히 진행되고 있다. 본 논문에서는 구매 선호도에 따른 상품 추천시스템의 분석방법을 개인 식품섭취 선호도에 따른 고혈압 및 당뇨 발생위험도를 분석하는 시스템에 적용하는 방법을 제안하고자 한다. 개인의 식품섭취 선호도 분석을 위해, 피어슨 상관계수를 이용하여 참조데이터와 샘플데이터의 유사도 가중치를 계산하고, 개인과의 유사도에 따른 집단을 구성하여 고혈압과 당뇨의 발생 위험도를 산출한다. 시스템의 유의성 검정을 위해 1,021명의 샘플을 시스템에 적용하였다. 고혈압과 당뇨병의 유병군에서 정상인군에 비해 더 높은 발생 위험도가 산출되는 통계적으로 유의한 경향을 확인할 수 있어 식품섭취 선호도와 고혈압/ 당뇨의 발생 위험도의 관련성이 있음을 확인하였다. 본 연구에서는 개인의 식품섭취 선호도에 따른 고혈압 및 당뇨 발생 위험도 분석 시스템의 유효성을 검증하였다.

Keywords

References

  1. C. H. Lee, S. Y. Lee and T. C. Chung, "Application recommender system based on personalized collaborative-filtering using user's emotion information from smartphone," KOREA INFORMATION SCIENCE SOCIETY, Vol.39 No.1A pp. 224-226, 2012.
  2. B. K. Lim and B. T. Zhang, "Personalized Menu Recommendation Algorithm using Hypernetwork," KOREA INFORMATION SCIENCE SOCIETY, Vol.39 No.1B pp. 393-395, 2012.
  3. D. H. Kim, S. H. Kim, K. Y. Cho and J. S. Cho, "u-DailyCare : Design and Implementation of prevention and Management Sevice for chronic and Adult disease," KOREA INFORMATION SCIENCE SOCIETY, Vol.39 No.1A pp. 197-199, 2012.
  4. J. H. Park, W. I. Park, Y. K. Kim and J. H. Kang, "A Customized Device Recommender System based on Context-Aware in Ubiquitous Environments," The Institute of Electronics Engineers of Korea - Computer and Information, Vol.46 No.3, pp. 15-23 , 2009.
  5. Korea Centers For Disease Control & Prevention, "Guideline: The Fifth Korea National Health and Nutrition Examination Survey (KNHANES V-2)", 2012.
  6. R. R.. Dholakia, interactivity and revisits to website, 2000, http://ritim.cbn.uri.edu/wp/
  7. J. K. Kim, J. H. Suh, D. H. Ann and Y. H. Cho, "A Personalized Recommendation Methodology based on Collaborative Filtering," INTELLIGENT SOCIETY, vol. 8, pp. 139-157, 2002.
  8. R. M. van Dam, E. B. Rimm, W. C. Willett, M. J. Stampfer, and F. B. Hu, "Dietary Patterns and Risk for Type 2 Diabetes Mellitus in U.S. Men," Annals of Internal Medicine, vol. 136, no. 3, pp. 201-209, 2002. https://doi.org/10.7326/0003-4819-136-3-200202050-00008
  9. S. H. Jeon and N. H. Kim, "Prevalence of Diabetes and Impaired fasting glucose according to Food frequency Similarity in Korea," Journal of the Korea Academia-Industrial cooperation Society, vol. 14, pp. 751-758, 2013. https://doi.org/10.5762/KAIS.2013.14.2.751
  10. S. H. Jeon and N. H. Kim, "Diabetes Risk Analysis Model with Personalized Food Intake Preference," Journal of the Korea Academia-Industrial cooperation Society, Vol. 14, No. 11, pp. 5771-5777, 2013. https://doi.org/10.5762/KAIS.2013.14.11.5771
  11. K. Y. Jung and J. H. Lee, "Comparative Evaluation of User Similarity Weight for Improving Prediction Accuracy in Personalized Recommender System," The Institute of Electronics Engineers of Korea - Computer and Information, Vol.42 No.6, pp. 63-74, 2005.