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

• DOI: 10.1371/journal.pone.0243229
• Publication Date: 2020-12-23T18:28:27Z
• Authors: Jun’ichi Kotoku; Asuka Oyama; Kanako Kitazumi; Hiroshi Toki; Akihiro Haga; Ryohei Yamamoto; Maki Shinzawa; Miyae Yamakawa; Sakiko Fukui; Keiichi Yamamoto; Toshiki Moriyama
• Keywords: Adult; Aged, 80 and over; Big Data; Male; Metabolic Syndrome; Science; Q; R; Age Factors; Normal Distribution; Middle Aged; 01 natural sciences; Sex Factors; Japan; Medicine; Humans; Female; 0101 mathematics; Algorithms; Medical Informatics; Research Article; Aged

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.