Artificial intelligence approaches to the determinants of women’s vaginal dryness using general hospital data

Abstract

The aim of this study is to analyse the determinants of women’s vaginal dryness using machine learning. Data came from Korea University Anam Hospital in Seoul, Republic of 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 vaginal dryness, measured by a Menopause Rating Scale. Random forest variable importance, a performance gap between a complete model and a model excluding a certain variable, was adopted for identifying major determinants of vaginal dryness. In terms of the mean squared error, the random forest (1.0597) was much better than linear regression (17.9043) and artificial neural networks with one, two and three hidden layers (1.7452, 1.7148 and 1.7736, respectively). Based on random forest variable importance, the top-10 determinants of vaginal dryness were menopause age, age, menopause, height, thyroid stimulating hormone, neutrophils, years since menopause, lymphocytes, alkaline phosphatase and blood urea nitrogen. In addition, its top-20 determinants were peak expiratory flow rate, low-density lipoprotein cholesterol, white blood cells, monocytes, cancer antigen 19-9, creatinine, eosinophils, total cholesterol, triglyceride and amylase. Machine learning presents a great decision support system for the prediction of vaginal dryness. For preventing vaginal dryness, preventive measures would be needed regarding early menopause, the thyroid function and systematic inflammation.

Impact Statement

• What is already known on this subject? Only a few studies have investigated the risk factors of vaginal dryness in middle-aged women. More research is to be done for finding its various risk factors, identifying its major risk groups and drawing its effective clinical implications.

• What do the results of this study add? This study is the first machine-learning study to predict women’s vaginal dryness and analyse their determinants. The random forest could discuss which factors are more important for the prediction of vaginal dryness. Based on random forest variable importance, menopause age was the most important determinant of vaginal dryness and their association was discovered to be negative in this study. Vaginal dryness was closely associated with the height, rather than the body weight or body mass index. The importance rankings of blood conditions related to systematic inflammation were within the top-20 in this study: neutrophils, lymphocytes, white blood cells, monocytes and eosinophils.

• What are the implications of these findings for clinical practice and/or further research? Machine learning presents a great decision support system for the prediction of vaginal dryness. For preventing vaginal dryness, preventive measures would be needed regarding early menopause and systematic inflammation.

Keywords:

• Vaginal dryness
• menopause age
• thyroid stimulating hormone
• inflammation marker

Ethics approval and consent to participate

This study was approved by the Institutional Review Board (IRB) of Korea University Anam Hospital on 20 January 2020 (2020AN0031). Informed consent was waived by the IRB.

Disclosure statement

The authors declare that they have no competing interests.

Author contributions

KR, KL, HP contributed to the design of the study. KR, KL, HP contributed to the collection, analysis and interpretation of the data. KR, KY, YK, JS, JH, TK, JS, KL, HP contributed to the writing of the manuscript.

Data availability statement

The datasets used and/or analysed during the current study are available from the corresponding authors on reasonable request.

Additional information

Funding

This work was supported by (1) the Ministry of Science and ICT of Korea (MSIT) under the Information Technology Research Centre support program supervised by the IITP (Institute for Information & Communications Technology Planning & Evaluation) [IITP-2018-0-01405] and (2) the National Research Foundation of Korea grant funded by the MSIT [No. 2020R1C1C1013579].