Impact of predicting health-guidance candidates using massive health check-up data: A data-driven analysis.

Journal: International journal of medical informatics
Published Date: Oct 1, 2017

Abstract

INTRODUCTION: Starting in 2008, specific health checkups and health guidance to prevent non-communicable diseases have been provided in Japan, which has the highest proportion of elderly citizens in the world. The attendance rate for health guidance appointments is 17.7%, which is far from the national goal of the system (45%). To improve the attendance rate, we present a model for predicting whether an examinee is a candidate for health guidance; this model was based on a machine learning method and a restricted but massive amount of health checkup information.

Authors

  • Daisuke Ichikawa
    SUSMED, Inc., Tokyo, Japan.
  • Toki Saito
    Dep. of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, Univ. of Tokyo, Japan.
  • Hiroshi Oyama
    Department of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan; Department of Clinical Information Engineering, School of Public Health, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan.

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