Chuinsiri Nontawat
Institute of Dentistry, Suranaree University of Technology, Nakhon Ratchasima, Thailand.
J Int Soc Prev Community Dent. 2021 Sep 21;11(5):531-538. doi: 10.4103/jispcd.JISPCD_131_21. eCollection 2021 Sep-Oct.
The aims of this study were to explore the use of unsupervised machine learning in clustering the population based on reports of oral pain, psychological distress, and sleep problems and to compare demographic and socio-economic characteristics as well as levels of functional domains (work, social, and leisure) between clusters.
In this cross-sectional study, a total of 1613 participants from the National Health and Nutrition Examination Survey in 2017-2018 were analyzed. Five variables, including oral pain, depression, anxiety, sleep apnea, and excessive daytime sleepiness, were selected for cluster analysis using the -medoids clustering algorithm. The distribution of categorical variables between clusters was assessed using χ test. One-way analysis of variance and Kruskal-Wallis test were used to compare numerical variables as appropriate.
Five distinct clusters were identified: healthy, norm, anxiety, apnea-comorbid, and pain-comorbid. The apnea-comorbid cluster had mean age of 59 years and higher proportion of men. The pain-comorbid cluster had mean age of 56 years and higher proportion of women. Whites constituted a majority of both comorbid clusters. The pain-comorbid cluster demonstrated the least percentage of individuals with college degree, the lowest income, and significant impairment in all functional domains.
Through the use of unsupervised machine learning, the clusters with comorbidity of oral pain, psychological distress, and sleep problems have emerged. Major characteristics of the comorbid clusters included mean age below 60 years, White, and low levels of education and income. Functional domains were significantly impaired. The comorbid clusters thus call for public health intervention.
本研究旨在探讨使用无监督机器学习根据口腔疼痛、心理困扰和睡眠问题报告对人群进行聚类，并比较各聚类之间的人口统计学和社会经济特征以及功能领域（工作、社交和休闲）水平。
在这项横断面研究中，对2017 - 2018年国家健康与营养检查调查中的1613名参与者进行了分析。使用 - 中位数聚类算法选择了五个变量，包括口腔疼痛、抑郁、焦虑、睡眠呼吸暂停和日间过度嗜睡，进行聚类分析。使用χ检验评估各聚类之间分类变量的分布。根据情况使用单因素方差分析和Kruskal - Wallis检验比较数值变量。
识别出五个不同的聚类：健康、正常、焦虑、呼吸暂停合并症和疼痛合并症。呼吸暂停合并症聚类的平均年龄为59岁，男性比例较高。疼痛合并症聚类的平均年龄为56岁，女性比例较高。两个合并症聚类中白人占多数。疼痛合并症聚类中拥有大学学位的个体比例最低，收入最低，并且在所有功能领域都有显著损害。
通过使用无监督机器学习，出现了口腔疼痛、心理困扰和睡眠问题合并症的聚类。合并症聚类的主要特征包括平均年龄低于60岁、白人、教育程度和收入水平较低。功能领域受到显著损害。因此，这些合并症聚类需要公共卫生干预。
