Abstract
This paper explores the cause-and-effect relationships among a set of health indices using causal discovery. The data we used to analyze was obtained from wearable devices, Traditional Chinese Medicine (TCM) diagnosis, and self-assessment of subjects in an experiment. Firstly, three machine learning algorithms were employed to address the issue of excessive missing values in the integrated dataset, and the coherence of this improved data was validated by statistical test. The NOTEARS algorithm was then employed to assess the causal relationships within the overall population as well as within subgroups based on gender, physical activity levels, and sleep duration. The results demonstrated that the NOTEARS algorithm yielded interesting and plausible outcomes, suggesting the presence of causal connections between variables of wearable devices and TCM diagnosis, as well as daily lifestyle habits.
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Acknowledgement
The work was supported in part by 2022–2024 Masaru Ibuka Foundation Research Project on Oriental Medicine, 2020–2025 JSPS A3 Foresight Pro-gram (Grant No. JPJSA3F20200001), 2022–2025 Japan National Initiative Promotion Grant for Digital Rural City, 2023 Waseda University Grants for Special Research Projects (No. 2023C-216), and 2023 Waseda University Advanced Research Center for Human Sciences Research Project (C) for Promoting Regional Cooperation.
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Li, Y., Deng, O., Ogihara, A., Nishimura, S., Jin, Q. (2023). Causal Discovery of Health Features from Wearable Device and Traditional Chinese Medicine Diagnosis Data. In: Gao, Q., Zhou, J., Duffy, V.G., Antona, M., Stephanidis, C. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14055. Springer, Cham. https://doi.org/10.1007/978-3-031-48041-6_37
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DOI: https://doi.org/10.1007/978-3-031-48041-6_37
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