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Does Last Year’s Cost Predict the Present Cost? An Application of Machine Leaning for the Japanese Area-Basis Public Health Insurance Database

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Listed:
  • Yoshiaki Nomura

    (Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Yoshimasa Ishii

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Yota Chiba

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Shunsuke Suzuki

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Akira Suzuki

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Senichi Suzuki

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Kenji Morita

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Joji Tanabe

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Koji Yamakawa

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Yasuo Ishiwata

    (Ebina Dental Association, Kanagawa 243-0421, Japan)

  • Meu Ishikawa

    (Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Kaoru Sogabe

    (Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Erika Kakuta

    (Department of Oral Microbiology, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Ayako Okada

    (Department of Operative Dentistry, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Ryoko Otsuka

    (Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

  • Nobuhiro Hanada

    (Department of Translational Research, Tsurumi University School of Dental Medicine, Yokohama 230-8501, Japan)

Abstract

The increasing healthcare cost imposes a large economic burden for the Japanese government. Predicting the healthcare cost may be a useful tool for policy making. A database of the area-basis public health insurance of one city was analyzed to predict the medical healthcare cost by the dental healthcare cost with a machine learning strategy. The 30,340 subjects who had continued registration of the area-basis public health insurance of Ebina city during April 2017 to September 2018 were analyzed. The sum of the healthcare cost was JPY 13,548,831,930. The per capita healthcare cost was JPY 446,567. The proportion of medical healthcare cost, medication cost, and dental healthcare cost was 78%, 15%, and 7%, respectively. By the results of the neural network model, the medical healthcare cost proportionally depended on the medical healthcare cost of the previous year. The dental healthcare cost of the previous year had a reducing effect on the medical healthcare cost. However, the effect was very small. Oral health may be a risk for chronic diseases. However, when evaluated by the healthcare cost, its effect was very small during the observation period.

Suggested Citation

  • Yoshiaki Nomura & Yoshimasa Ishii & Yota Chiba & Shunsuke Suzuki & Akira Suzuki & Senichi Suzuki & Kenji Morita & Joji Tanabe & Koji Yamakawa & Yasuo Ishiwata & Meu Ishikawa & Kaoru Sogabe & Erika Kak, 2021. "Does Last Year’s Cost Predict the Present Cost? An Application of Machine Leaning for the Japanese Area-Basis Public Health Insurance Database," IJERPH, MDPI, vol. 18(2), pages 1-11, January.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:2:p:565-:d:478620
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    References listed on IDEAS

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    1. Andrew M. Jones & James Lomas & Nigel Rice, 2015. "Healthcare Cost Regressions: Going Beyond the Mean to Estimate the Full Distribution," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1192-1212, September.
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