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Machine Learning Approaches to Identify Factors Associated with Women's Vasomotor Symptoms Using General Hospital Data

Authors
Ryu, Ki JinYi, Kyong WookKim, Yong JinShin, Jung HoHur, Jun YoungKim, TakSeo, Jong BaeLee, Kwang SigPark, Hyuntae
Issue Date
3-5월-2021
Publisher
KOREAN ACAD MEDICAL SCIENCES
Keywords
Cancer Antigen; Hot Flashes; Menopause Age; Monocyte; Thyroid Stimulating Hormone; Vasomotor Symptoms
Citation
JOURNAL OF KOREAN MEDICAL SCIENCE, v.36, no.17, pp.1 - 11
Indexed
SCIE
SCOPUS
KCI
Journal Title
JOURNAL OF KOREAN MEDICAL SCIENCE
Volume
36
Number
17
Start Page
1
End Page
11
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/137393
DOI
10.3346/jkms.2021.36.e122
ISSN
1011-8934
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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