Department of Obstetrics & Gynecology, Korea University College of Medicine, Seoul, Republic of Korea.
Department of Biosciences, Mokpo National University, Muan-gun, Republic of Korea.
J Obstet Gynaecol. 2022 Jul;42(5):1518-1523. doi: 10.1080/01443615.2021.2013785. Epub 2022 Jan 7.
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 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. 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. 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.
本研究旨在利用机器学习分析女性阴道干燥的决定因素。数据来自韩国首尔高丽大学安岩医院，共纳入 3298 名年龄 40-80 岁的女性，她们于 2010 年 1 月至 2012 年 12 月期间进行了常规健康检查。本研究应用了 5 种机器学习方法，并比较了它们对阴道干燥的预测能力，阴道干燥采用绝经评定量表进行评估。随机森林变量重要性是一种完整模型与排除特定变量的模型之间性能差距的指标，用于识别阴道干燥的主要决定因素。就均方误差而言，随机森林（1.0597）明显优于线性回归（17.9043）和具有 1、2 和 3 个隐藏层的人工神经网络（1.7452、1.7148 和 1.7736）。基于随机森林变量重要性，阴道干燥的前 10 个决定因素为绝经年龄、年龄、绝经、身高、促甲状腺激素、中性粒细胞、绝经年限、淋巴细胞、碱性磷酸酶和血尿素氮。此外，前 20 个决定因素包括呼气峰值流速、低密度脂蛋白胆固醇、白细胞、单核细胞、癌抗原 19-9、肌酐、嗜酸性粒细胞、总胆固醇、甘油三酯和淀粉酶。机器学习为阴道干燥的预测提供了一个很好的决策支持系统。为了预防阴道干燥，需要针对早绝经、甲状腺功能和系统性炎症采取预防措施。
影响陈述 只有少数研究调查了中年女性阴道干燥的危险因素。需要进一步研究以发现其各种危险因素，确定其主要风险人群，并得出其有效的临床意义。本研究是首次采用机器学习预测女性阴道干燥并分析其决定因素的研究。随机森林可以讨论哪些因素对阴道干燥的预测更为重要。基于随机森林变量重要性，绝经年龄是阴道干燥的最重要决定因素，本研究发现两者之间存在负相关关系。阴道干燥与身高密切相关，而与体重或体重指数无关。本研究中，与系统性炎症相关的血液状况的重要性排名在前 20 位：中性粒细胞、淋巴细胞、白细胞、单核细胞和嗜酸性粒细胞。机器学习为阴道干燥的预测提供了一个很好的决策支持系统。为了预防阴道干燥，需要针对早绝经和系统性炎症采取预防措施。
