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Bayesian Network Modeling for Specific Health Checkups on Metabolic Syndrome

  • Yoshiaki Miyauchi
  • Haruhiko NishimuraEmail author
Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 137)

Abstract

Metabolic syndrome has become a significant public health problem worldwide, and Specific Health Checkup and Guidance on this syndrome began for people aged 40 to 74 in Japan in 2008. Through this guidance, people considered at high risk of developing metabolic syndrome are expected to be made aware of their own problems in terms of their daily lifestyle choices and to improve their daily life behaviors by themselves. To support this large undertaking with information technology, we have introduced ideas based on the Bayesian estimation in data mining technology and proposed a Bayesian network (BN) scheme connecting the information from physical examinations and daily lifestyle questionnaires. By applying this network model to the field data on 11,947 anonymized individuals, the proposed method was found to provide better performance and show its potentiality for the system of specific health checkup. We introduced a novel 4-bit representation with 16 states, treating body shape, blood lipids, blood glucose, and blood pressure as equal binary factors, and analyzed relationships among the support level, physical examination, and daily lifestyle questionnaire. In addition, we applied this BN to individual cases and showed its utility in allowing an examinee to improve his/her lifestyle by demonstrating individual predictions. Through the efforts described above, we confirmed that the Bayesian network for Specific Health Checkup and Guidance has the potential to be an effective support tool for health promotion regarding metabolic syndrome.

Keywords

Metabolic syndrome Bayesian networks Specific health checkup Health guidance Health promotion 

References

  1. 1.
    Specific Health Checkups and Specific Health Guidance: The Health Service Bureau of the Ministry of Health, Labour and Welfare. (2007)Google Scholar
  2. 2.
    Tamura, T., Kimura, Y.: Specific health checkups in Japan: The present situation analyzed using 5-year statistics and the future. Biomed. Eng. Lett. 5(1), 22–28 (2015)CrossRefGoogle Scholar
  3. 3.
    Miyauchi, Y., Nishimura, H.: Bayesian network for healthcare of metabolic syndrome, IEEE EMBC2013, Osaka, Short paper No. 3164. (2013)Google Scholar
  4. 4.
    Miyauchi, Y., Nishimura, H.: Construction and evaluation of Bayesian networks related to the specific health checkup and guidance on metabolic syndrome, innovation in medicine and healthcare 2015 (Smart Innovation, Systems and Technologies, Vol. 45) Y.W. Chen et al. (Eds.), pp. 183–193, Springer International Publishing (2015)Google Scholar
  5. 5.
    Miyauchi, Y., Nishimura, H., Inada, H.: Analysis of interannual data for the specific health checkup to develop its Bayesian network application. Health Eval. Promot. 42(5), 479–491 (2015) (in Japanese)CrossRefGoogle Scholar
  6. 6.
    Miyauchi, Y., Nishimura, H., Nakano, Y.: A study of Bayesian Network model related to the specific health checkup based on lifestyle factor analysis. Trans. Jpn Soc. of Kansei Eng. 15(7), 693–701 (2016) (in Japanese)CrossRefGoogle Scholar
  7. 7.
    Park H. S., Cho,S. B.: An efficient attribute ordering optimization in Bayesian Net-works for prognostic modeling of the metabolic syndrome, ICIC2006, LNBI4115, pp. 381−391. Springer (2006)Google Scholar
  8. 8.
    Maglogiannis, I., Zafiropoulos, E.: A. platis and C. Lambrinoudakis, risk analysis of a patient monitoring system using Bayesian Network modeling. J. Biomed. Inform. 39(6), 637–647 (2006)CrossRefGoogle Scholar
  9. 9.
    Lee S. M., Abbott, P. A.: Bayesian Network for knowledge discovery in large datasets. J. Biomed. inform. 36, 389−399 (2003)Google Scholar
  10. 10.
    Fuster-Parra, P., Tauler, P., Bennasar-Veny, M., Ligęza, A., López-González, A.A., Aguiló, A.: Bayesian Network modeling: A case study of an epidemiologic system analysis of cardiovascular risk. Comput. Methods Programs Biomed. 126, 128–142 (2016)CrossRefGoogle Scholar
  11. 11.
    Sambo, F., Facchinetti, A., Hakaste, L., Kravic, J., Di Camillo, B., Fico, G., Cobelli, C.: A Bayesian Network for probabilistic reasoning and imputation of missing risk factors in type 2 diabetes. In Conference on Artificial Intelligence in Medicine in Europe, pp. 172–176, Springer, Cham (2015)Google Scholar
  12. 12.
    Barakat, N.: Diagnosis of Metabolic Syndrome: A Diversity Based Hybrid Model, Machine Learning and Data Mining in Pattern Recognition, pp. 185–198. Springer International Publishing (2016)Google Scholar
  13. 13.
    Zhao, C., Jiang, J., Xu, Z., Guan, Y.: A study of EMR-based medical knowledge network and its applications. Comput. Methods Programs Biomed. 143, 13–23 (2017)CrossRefGoogle Scholar
  14. 14.
    Babič, F., Majnarić, L., Lukáčová, A., Paralič, J., Holzinger, A.: On patient’s characteristics extraction for metabolic syndrome diagnosis: Predictive modelling based on machine learning. In International Conference on Information Technology in Bio-and Medical Informatics, pp. 118–132. Springer, Cham (2014)Google Scholar
  15. 15.
    Shen, B., Todaro, J.F., Niaura, R., McCaffery, J.M., Zhang, J., Spiro III, A., Ward, K.D.: Are metabolic risk factors one unified syndrome? Model. Struct. Metab. Syndr. X, Am. J. Epidemiol. 157, 701–711 (2003)Google Scholar
  16. 16.
    Shah, S., Novak, S., Stapleton, L.M.: Evaluation and comparison of models of metabolic syndrome using confirmatory factor analysis. Eur. J. Epidemiol. 21, 343–349 (2006)CrossRefGoogle Scholar
  17. 17.
    Netica User’s Guide: http://www.norsys.com/. Application for belief network and influence diagrams

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.School of NursingNagoya City UniversityMizuho-Ku, NagoyaJapan
  2. 2.School of Applied InformaticsUniversity of HyogoKobeJapan

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