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.
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Miyauchi, Y., Nishimura, H. (2018). Bayesian Network Modeling for Specific Health Checkups on Metabolic Syndrome. In: Holmes, D., Jain, L. (eds) Advances in Biomedical Informatics. Intelligent Systems Reference Library, vol 137. Springer, Cham. https://doi.org/10.1007/978-3-319-67513-8_5
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DOI: https://doi.org/10.1007/978-3-319-67513-8_5
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