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

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 tria...

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Detalles Bibliográficos
Autores principales: Kotoku, Jun’ichi, Oyama, Asuka, Kitazumi, Kanako, Toki, Hiroshi, Haga, Akihiro, Yamamoto, Ryohei, Shinzawa, Maki, Yamakawa, Miyae, Fukui, Sakiko, Yamamoto, Keiichi, Moriyama, Toshiki
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757823/
https://www.ncbi.nlm.nih.gov/pubmed/33362207
http://dx.doi.org/10.1371/journal.pone.0243229

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7757823/
https://www.ncbi.nlm.nih.gov/pubmed/33362207
http://dx.doi.org/10.1371/journal.pone.0243229

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