Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data
- Authors
- Ryu Ki-Jin; Yi Kyong Wook; Kim Yong Jin; Shin Jung Ho; Hur Jun Young; Kim Tak; Seo Jong Bae; Lee Kwang-Sig; Park Hyuntae
- Issue Date
- 3-May-2021
- Publisher
- 대한의학회
- Keywords
- Vasomotor Symptoms; Hot Flashes; Menopause Age; Thyroid Stimulating Hormone; Monocyte; Cancer Antigen
- Citation
- Journal of Korean Medical Science, v.36, no.17, pp 1 - 9
- Pages
- 9
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- Journal of Korean Medical Science
- Volume
- 36
- Number
- 17
- Start Page
- 1
- End Page
- 9
- URI
- https://scholarworks.korea.ac.kr/kumedicine/handle/2020.sw.kumedicine/53157
- DOI
- 10.3346/jkms.2021.36.e122
- ISSN
- 1011-8934
1598-6357
- Abstract
- Background: To analyze the factors associated with women's vasomotor symptoms (VMS) using machine learning.
Methods: Data on 3,298 women, aged 40–80 years, who attended their general health check-up from January 2010 to December 2012 were obtained from Korea University Anam Hospital in Seoul, Korea. Five machine learning methods were applied and compared for the prediction of VMS, measured by the Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying the major factors associated with VMS.
Results: In terms of the mean squared error, the random forest (0.9326) was much better than linear regression (12.4856) and artificial neural networks with one, two, and three hidden layers (1.5576, 1.5184, and 1.5833, respectively). Based on the variable importance from the random forest, the most important factors associated with VMS were age, menopause age, thyroid-stimulating hormone, and monocyte, triglyceride, gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19-9, C-reactive protein, and low-density lipoprotein cholesterol levels. Indeed, the following variables were ranked within the top 20 in terms of variable importance: cancer antigen 125, total cholesterol, insulin, free thyroxine, forced vital capacity, alanine aminotransferase, forced expired volume in 1 second, height, homeostatic model assessment for insulin resistance, and carcinoembryonic antigen.
Conclusion: Machine learning provides an invaluable decision support system for the prediction of VMS. For managing VMS, comprehensive consideration is needed regarding thyroid function, lipid profile, liver function, inflammation markers, insulin resistance, monocyte count, cancer antigens, and lung function.
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- Appears in
Collections - 4. Research institute > Institute of Human Behavior and Genetics > 1. Journal Articles
- 2. Clinical Science > Department of Obstetrics and Gynecology > 1. Journal Articles
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