Abstract
Background
To analyze the determinants of women’s vasomotor symptoms (VMS) using machine learning.
Methods
Data came from Korea University Anam Hospital in Seoul, Korea, with 3298 women, aged 40–80 years, who attended their general health check from January 2010 to December 2012. Five machine learning methods were applied and compared for the prediction of VMS, measured by a Menopause Rating Scale. Variable importance, the effect of a variable on model performance, was used for identifying major determinants of 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 variable importance from the random forest, the most important determinants of VMS were age, menopause age, thyroid stimulating hormone, monocyte and triglyceride, as well as gamma glutamyl transferase, blood urea nitrogen, cancer antigen 19 − 9, C-reactive protein and low-density-lipoprotein cholesterol. Indeed, the following determinants 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 one second, height, homeostatic model assessment for insulin resistance and carcinoembryonic antigen.
Conclusions
Machine learning provides an invaluable decision support system for the prediction of VMS. For preventing VMS, preventive measures would be needed regarding the thyroid function, the lipid profile, the liver function, inflammation markers, insulin resistance, the monocyte, cancer antigens and the lung function.
Ensemble learning methods for decision making: Status and future prospects
