Study on the facial spectrum and color characteristics of patients with essential hypertension  

原发性高血压患者面色光谱和颜色特征研究

作　　者：FU Hongyuan CHUN Yi JIAO Wen SHI Yulin TU Liping LI Yongzhi XU Jiatuo 付洪媛;春意;焦文;石玉琳;屠立平;李勇枝;许家佗(上海中医药大学中医学院,上海201203;上海市中医医院临床研究中心,上海200071;上海中医药大学教务处,上海201203;中国航天员科研训练中心航天员健康中心实验室,北京100094)

机构地区：[1]School of Traditional Chinese Medicine,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China [2]Clinical Research Center,Shanghai Hospital of Traditional Chinese Medicine,Shanghai 200071,China [3]Office of Academic Affairs,Shanghai University of Traditional Chinese Medicine,Shanghai 201203,China [4]Astronaut Health Center Laboratory,China Astronaut Research and Training Center,Beijing 100094,China

出　　处：《Digital Chinese Medicine》2024年第4期429-440,I0002-I0006,共17页数字中医药(英文)

基　　金：National Natural Science Foundation of China(82104738 and 82004255);Key Discipline Construction Project of High-level Traditional Chinese Medicine of the National Administration of Traditional Chinese Medicine-Traditional Chinese Medical Diagnostics(ZYYZDXK-2023069).

摘　　要：Objective To investigate the facial spectrum and color characteristics of patients with essen-tial hypertension post administering antihypertensive drugs,establish a classification and evaluation model based on the facial colors of the enrolled patients,and perform in-depth analysis on the important characteristics of their facial spectrum.Methods From September 3,2018,to March 23,2024,participants with essential hyperten-sion(receiving antihypertensive medication treatment,hypertension group)and normal blood pressure(control group)were recruited from the Cardiology Department of Shanghai Hospital of Traditional Chinese Medicine,the Coronary Care Unit of Shanghai Tenth People's Hospital,the Physical Examination Center of Shuguang Hospital Affiliated to Shanghai Uni-versity of Traditional Chinese Medicine,and the Gaohang Community Health Service Center.This study employed the propensity score matching(PSM)method to reduce study partici-pants selection bias.Spectral information in the facial visible light spectrum of the subjects was collected using a flame spectrometer,and the spectral chromaticity values were calculat-ed using the equal-interval wavelength method.The study analyzed the differences in spec-tral reflectance across various facial regions,including the entire face,forehead,glabella,nose,jaw,left and right zygomatic regions,left and right cheek regions as well as differences in parameters within the Lab color space between the two subject groups.Feature selection was conducted using least absolute shrinkage and selection operator(LASSO)regression,fol-lowed by the application of various machine learning algorithms,including logistic regres-sion(LR),support vector machine(SVM),random forest(RF),Naïve Bayes(NB),and eX-treme Gradient Boosting(XGB).The reduced-dimensional dataset was split in a 7:3 ratio to establish a classification and assessment model for facial coloration related to primary hyper-tension.Additionally,model fusion techniques were applied to enhance the predictive power.The performance 目的探讨原发性高血压患者在服用降压药后的面色光谱和颜色特征,联合机器学习算法建立一个原发性高血压面色分类评估模型并对面色光谱特征重要性进行进一步分析。方法于2018年9月3日至2024年3月23日期间,分别在上海市中医院心内科、上海市第十人民医院冠心病监护病房、上海中医药大学附属曙光医院体检中心和高行社区卫生服务中心招募原发性高血压患者(接受降压药物治疗,高血压组)和正常血压受试者(对照组)。本研究使用倾向性评分匹配方法对受试者进行匹配。使用Flame光谱仪采集受试者的面部可见光光谱信息,并利用等间隔波长法计算光谱色度值。本研究分析了两组受试者在面部整体、前额、眉间、鼻部、下巴、两颧部和两颊部的光谱反射率以及Lab色空间中参数的差异。使用最小绝对收缩和选择算子(LASSO)回归进行特征筛选,利用逻辑回归(LR)、支持向量机(SVM)、随机森林(RF)、朴素贝叶斯(NB)、极限梯度提升(XGB)等多种机器学习算法,将降维后的数据集按7:3的比例划分,建立原发性高血压面色分类评估模型,同时进行模型融合。以曲线下面积(AUC)、准确度等指标评估模型性能。使用夏普利加性解释(SHAP)来解释模型结果。结果高血压组和对照组各纳入了114名研究对象。面部整体和八个采集区域的反射率分析显示,与对照组相比,高血压组在蓝紫光区域对相应色光的反射率高(P<0.05),在红光区域对相应色光的反射率低(P<0.05)。面部整体和八个采集区域的Lab色空间参数分析显示,高血压组a值、b值均小于对照组(P<0.05)。经过LASSO回归筛选,共有包括颏部的a值、右颊部的a值、庭部380 nm和780 nm的反射率等在内的18个面色特征被认为与高血压高度相关。多模型分类结果显示,RF分类模型为最优模型,AUC为0.74,准确率为0.77。RF+LR+SVM模型融合较单一模型的分类�

关 键 词：Essential hypertension Complexion Visible spectrum Machine learning Shapley Additive exPlanations(SHAP) 

分 类 号：R54[医药卫生—心血管疾病]