Prediction of Dietary Knowledge using Multiple Regression Analysis for Preventing Stomach Diseases

위장질환 예방을 위한 다중회귀분석을 이용한 식이지식 예측

  • Choi, So-Young (Data Mining Lab., Department of Computer Science, Kyonggi University) ;
  • Kim, Joo-Chang (Data Mining Lab., Department of Computer Science, Kyonggi University) ;
  • Chung, Kyungyong (Division of Computer Science and Engineering, Kyonggi University)
  • 최소영 (경기대학교 컴퓨터과학과) ;
  • 김주창 (경기대학교 컴퓨터과학과) ;
  • 정경용 (경기대학교 컴퓨터공학부)
  • Received : 2019.03.22
  • Accepted : 2019.07.20
  • Published : 2019.07.28


Modern society is undergoing nutritional imbalance according to the diet as the number of one person increases. This is increasing the incidence of chronic diseases such as gastrointestinal diseases and digestive diseases. This study suggests the prediction of dietary knowledge using multiple regression analysis for preventing chronic stomach diseases. The proposed method manages user's stomach diseases and dietary nutrition through the prediction of nutrition knowledge. It collects user's PHR through smart device and integrates in the health platform. The integrated data analyzes the dietary and activity of the user through multiple regression analysis. It predicts the required nutrients and provides services to users through applications. Therefore, it suggests recommended dietary components and consumed calories, appropriate dietary components based on the user's basal metabolism, and gastrointestinal levels. With the personalized health management, modern people can manage gastrointestinal diseases through a balanced diet.

OHHGBW_2019_v10n7_1_f0001.png 이미지

Fig. 1. Management of Nutrition Knowledge using Multiple Regression Analysis

Table 1. Definition of variables

OHHGBW_2019_v10n7_1_t0001.png 이미지

Table 2. Preprocessed dietary data

OHHGBW_2019_v10n7_1_t0002.png 이미지

Table 3. Performance evaluation

OHHGBW_2019_v10n7_1_t0003.png 이미지


Supported by : Gyeonggi province


  1. W. Raghupathi & V. Raghupathi. (2014). Big Data Analytics in Healthcare: Promise and Potential. Health Information Science and Systems, 2(1), 3.
  2. H. S. Schwerin, J. L. Stanton, J. L. Smith, A. M. Riley Jr & B. E. (1982). Food, Eating Habits, and Health: a Further Examination of the Relationship Between Food Eating Patterns and Nutritional Health. The American Journal of Clinical Nutrition, 35(5), 1319-1325.
  3. Health Insurance Review & Assessment Service(HIRA). (2018),
  4. M. E. Shils & M. Shike. (2006). Modern nutrition in health and disease. Lippincott Williams & Wilkins.
  5. D. Krackhardt. (1988). Predicting with networks: Nonparametric multiple regression analysis of dyadic data. Social networks, 10(4), 359-381.
  6. D. Kaelber & E. C. Pan. (2008). The Value of Personal Health Record (PHR) Systems. In AMIA Annual Symposium Proceedings. (2008. p. 343). American Medical Informatics Association.
  7. F. C. Collins & H. Varmus. (2015). A New Initiative on Precision Medicine. New England Journal of Medicine, 372(9), 793-795.
  8. G. P. Zhang. (2003). Time Series Forecasting using a Hybrid ARIMA and Neural Network model. Neurocomputing, 50, 159-175.
  9. J. Contreras, R. Espinola, F. J. Nogales & A. J. Conejo. (2003). ARIMA Models to Predict Next-day Electricity Prices. IEEE Transactions on Power Systems, 18(3), 1014-1020.
  10. E. Y. Jung, J. H. Kim, K. Chung & D. K. Park. (2013). Home Health Gateway based Healthcare Services through U-health Platform. Wireless Personal Communications, 73(2), 207-218.
  11. National Information Society Agency, ICT-based Nutrition Management Service Empirical Data. (2018).
  12. C. H. Mason & W. D. Perreault Jr. (1991). Collinearity, Power, and Interpretation of Multiple Regression Analysis. Journal of Marketing Research, 28(3), 268-280.
  13. J. Kim & K. Chung. (2014). Ontology-based Healthcare Context Information Model to Implement Ubiquitous Environment. Multimedia Tools and Applications, 71(2), 873-888.
  14. H. Jung & K. Chung. (2016). Knowledge-based Dietary Nutrition Recommendation for Obese Management. Information Technology and Management, 17(1), 29-42.
  15. J. C. Kim & K. Chung. (2018). Mining Health-risk Factors using PHR Similarity in a Hybrid P2P Network. Peer-to-Peer Networking and Applications, 11(6), 1278-1287.
  16. J. C. Kim & K. Chung. (2019). Prediction Model of User Physical Activity using Data Characteristics-based Long Short-term Memory Recurrent Neural Networks. KSII Transactions on Internet and Information Systems, 13(4), 2060-2077.