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

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 Kakuta, Ayako Okada, Ryoko Otsuka and Nobuhiro Hanada
Additional contact information
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

IJERPH, 2021, vol. 18, issue 2, 1-11

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.

Keywords: healthcare cost; medical healthcare cost; dental healthcare cost; zero-inflated model; neural network (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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