2020
DOI: 10.21203/rs.3.rs-74144/v1
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Machine learning approaches to the determinants of women’s vasomotor symptoms using general hospital data

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… Show more

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“…Although in linear regression MSE was almost lower than Rforest, the correlation coefficient denotes the better performance of Rforest. So, our findings confirm the study of Ryu et al [78], in which Rforest was more accurate than linear regression. however, in some other studies linear regression outperformed Rforest [79].…”
Section: Plos Onesupporting
confidence: 92%
“…Although in linear regression MSE was almost lower than Rforest, the correlation coefficient denotes the better performance of Rforest. So, our findings confirm the study of Ryu et al [78], in which Rforest was more accurate than linear regression. however, in some other studies linear regression outperformed Rforest [79].…”
Section: Plos Onesupporting
confidence: 92%