摘要
为了探讨基于舌诊的疾病快速筛查,运用可见和近红外光谱仪,采集149名志愿者舌尖的反射光谱并且进行反射率归一化处理。根据临床诊断结果将样本分为4组:健康组、高粘血症倾向组、脂肪肝患者组和冠心病患者组。运用主成分分析(PCA)结合人工神经网络(ANN)方法、偏最小二乘(PLS)方法和间隔偏最小二乘(iPLS)方法3种方法建立分类预测模型。预测准确率分别为75%,75%和85%。实验结果表明,在3种建模方法中,iPLS预测效果最好,与可见光波段相比,近红外波段含有更多与疾病分类相关的光谱信息。实验的结果表明,光谱法用于某些疾病的快速诊断具有较高的可行性。
To screen disease which based on tongue inspection rapidly,the reflection spectrum on the tongue tips of 149 volunteers were collected by visible and near-infrared spectrometer and then the normalized reflectivity was calculated.Samples were divided into four classes according to the clinical diagnosis information： healthy,hyperviscosity,fatty liver,and coronary heart disease groups.Spectra were then subjected to three different analysis methods： principle component analysis（PCA） combined with artificial neural network（ANN）,partial least squares（PLS）,and interval PLS（iPLS）.The classification accuracy of each model are 75%,75%,and 85%,respectively.The results show that iPLS method sees more robust than the others.And the results also show that near-infrared region including more disease information than visible region.Experimental results show that the application of the spectra for disease diagnosis is promising.
出处
《光学学报》
EI
CAS
CSCD
北大核心
2011年第3期183-188,共6页
Acta Optica Sinica
基金
国家自然科学基金(30973964)
天津市应用基础及其前沿技术研究计划(10JCYBJC00400)资助课题
关键词
光谱学
疾病诊断
主成分分析
人工神经网络
偏最小二乘法
间隔偏最小二乘法
spectroscopy
disease diagnosis
principal component analysis（PCA）
artificial neural network（ANN）
partial least square（PLS）
interval partial least square（iPLS）
