Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.

Journal: International journal of medical informatics

PMID: 29425640

Abstract

OBJECTIVES: Since the launch of a nationwide general health check-up and instruction program in Japan in 2008, interest in strategies to improve implementation of the program based on predictive analytics has grown. We investigated the performance of prediction models developed to identify individuals classified as "requiring instruction" (high-risk) who were unlikely to participate in a health intervention program.

Authors

Akihiro Shimoda

Department of Clinical Information Engineering, Division of Social Medicine, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan. Electronic address: shimoda-tky@umin.ac.jp.
Daisuke Ichikawa

SUSMED, Inc., 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.

Keywords

Adult Aged Algorithms Early Intervention, Educational Female Health Behavior Health Promotion Humans Japan Logistic Models Machine Learning Male Metabolic Syndrome Middle Aged Retrospective Studies Risk Factors

External Resources

View on PubMed Access via DOI PubMed (29425640)

Prediction models to identify individuals at risk of metabolic syndrome who are unlikely to participate in a health intervention program.

Abstract

Authors

Keywords

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