Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups

• Kotoku, Jun’ichi

• Oyama, Asuka

• Kitazumi, Kanako

• Toki, Hiroshi

• Haga, Akihiro

• Yamamoto, Ryohei

• Shinzawa, Maki

• Yamakawa, Miyae

• Fukui, Sakiko

• Yamamoto, Keiichi

• Moriyama, Toshiki

• Praveen Rao

  editor

Bibliographic Information

Other Title

• Causal relations of health indices inferred statistically using the DirectLiNGAM

Abstract

Causal relations among many statistical variables have been assessed using a Linear non-Gaussian Acyclic Model (LiNGAM). Using access to large amounts of health checkup data from Osaka prefecture obtained during the six fiscal years of years 2012–2017, we applied the DirectLiNGAM algorithm as a trial to extract causal relations among health indices for age groups and genders. Results show that LiNGAM yields interesting and reasonable results, suggesting causal relations and correlation among the statistical indices used for these analyses.

Journal

• PLOS ONE

  PLOS ONE 15 (12), e0243229-, 2020-12-23

  PLOS

Keywords

• Multidisciplinary

Details

• CRID: 1050008832604663808
• NII Article ID: 120007174424
• ISSN: 19326203
• DOI: 10.1371/journal.pone.0243229
• Web Site: http://repo.lib.tokushima-u.ac.jp/116467
• Web Site: https://dx.plos.org/10.1371/journal.pone.0243229
• Text Lang: en
• Article Type: journal article
• Data Source:
  • IRDB
  • Crossref
  • CiNii Articles
  • KAKEN