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A Data-Driven Procedure of Providing a Health Promotion Program for Hypertension Prevention

Author

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  • Junghye Lee

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea; School of Management Engineering, Ulsan National Institute of Science and Technology, Ulsan 44919, Korea)

  • Ryeok-Hwan Kwon

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

  • Hyung Woo Kim

    (Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea)

  • Sung-Hong Kang

    (Department of Health Policy and Management, Inje University, Gimhae 50834)

  • Kwang-Jae Kim

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

  • Chi-Hyuck Jun

    (Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang 37673, Korea)

Abstract

We propose a two-step procedure based on data analytics to help service providers to efficiently and effectively implement a health promotion program to prevent hypertension. First, we developed a prediction model to identify people who are at risk for developing hypertension. Then, to eliminate specific risk factors for each of these individuals, we proposed four methods to create an index that represents the importance of each intervention program, which is a subprogram of the health promotion program. This index can be used to recommend appropriate intervention programs for each individual. We used the national sample cohort database of South Korea to offer a case study of the implementation of the proposed procedure. The constructed prediction model using logistic regression has adequate accuracy, and the proposed index that uses different methods has similar results to those of a doctor. This two-step procedure by automatic modeling based on data will be useful to save human resources and to provide informative and personalized results based on individual healthcare records.

Suggested Citation

  • Junghye Lee & Ryeok-Hwan Kwon & Hyung Woo Kim & Sung-Hong Kang & Kwang-Jae Kim & Chi-Hyuck Jun, 2018. "A Data-Driven Procedure of Providing a Health Promotion Program for Hypertension Prevention," Service Science, INFORMS, vol. 10(3), pages 289-301, September.
    • Handle: RePEc:inm:orserv:v:10:y:2018:i:3:p:289-301
    DOI: serv.2018.0220
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    References listed on IDEAS

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    1. Justin B Echouffo-Tcheugui & G David Batty & Mika Kivimäki & Andre P Kengne, 2013. "Risk Models to Predict Hypertension: A Systematic Review," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-10, July.
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    Cited by:

    1. Gang Du & Xi Liang & Xiaoling Ouyang & Chunming Wang, 2021. "Risk prediction of hypertension complications based on the intelligent algorithm optimized Bayesian network," Journal of Combinatorial Optimization, Springer, vol. 42(4), pages 966-987, November.
    2. Lisa M. Maillart & Maria E. Mayorga, 2018. "Introduction to the Special Issue on Advancing Health Services," Service Science, INFORMS, vol. 10(3), pages 1-1, September.

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