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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...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
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Materias: | |
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 |
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author | Kotoku, Jun’ichi Oyama, Asuka Kitazumi, Kanako Toki, Hiroshi Haga, Akihiro Yamamoto, Ryohei Shinzawa, Maki Yamakawa, Miyae Fukui, Sakiko Yamamoto, Keiichi Moriyama, Toshiki |
author_facet | Kotoku, Jun’ichi Oyama, Asuka Kitazumi, Kanako Toki, Hiroshi Haga, Akihiro Yamamoto, Ryohei Shinzawa, Maki Yamakawa, Miyae Fukui, Sakiko Yamamoto, Keiichi Moriyama, Toshiki |
author_sort | Kotoku, Jun’ichi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7757823 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-77578232021-01-06 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 PLoS One Research Article 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. Public Library of Science 2020-12-23 /pmc/articles/PMC7757823/ /pubmed/33362207 http://dx.doi.org/10.1371/journal.pone.0243229 Text en © 2020 Kotoku et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Kotoku, Jun’ichi Oyama, Asuka Kitazumi, Kanako Toki, Hiroshi Haga, Akihiro Yamamoto, Ryohei Shinzawa, Maki Yamakawa, Miyae Fukui, Sakiko Yamamoto, Keiichi Moriyama, Toshiki Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title | Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title_full | Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title_fullStr | Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title_full_unstemmed | Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title_short | Causal relations of health indices inferred statistically using the DirectLiNGAM algorithm from big data of Osaka prefecture health checkups |
title_sort | causal relations of health indices inferred statistically using the directlingam algorithm from big data of osaka prefecture health checkups |
topic | Research Article |
url | 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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