Hiroshi Oyama

Department of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

Publications

• Original Article   
  Identifying the Most Appropriate Intervention Targets Using Prediction Model Based on a Machine-Learning Method: A Retrospective Analysis of a Health Promotion Program for Improving Participation in General Health Check-Up
  Author(s): Akihiro Shimoda*, Yoshiyuki Saito, Chieko Ooe, Daisuke Ichikawa, Ataru Igarashi, Takeo Nakayama, Toki Saito and Hiroshi Oyama
  
  Objective: We aimed to identify the population in which encouraging participation in the general health check-up would be helpful using a prediction model based on a machine-learning method. A secondary analysis of data obtained from the health promotion program using a randomized controlled design, aimed at improving participation in the general health check-up, was performed. Methods: The retrospective analysis was conducted using data from a health promotion program in the Fukuoka branch of Japan Health Insurance Association, Japan, between November 2015 and March 2016. Subjects were extracted from dependents (family members) of insured persons aged 40-74 years who had participated in general health check-up at least once in the past five years (2010-2014). Subjects were divided into two groups; the intervention group received a printed rem.. Read More»
  DOI: 10.24105/ejbi.2018.14.4.8