Objective: In this paper, the classification quantification and intelligent identification of traditional Chinese medicine (TCM) syndromes of hypertension based on facial image information are studied by the method of combination of medicine and engineering. 

 

Methods: Firstly, the research on the combination of TCM syndrome quantification and hypertension blood pressure levels and the application of chromatographic decomposition technology to the extraction of TCM facial features are innovatively explored. Secondly, on the basis of the above research and combining with the machine learning algorithm, the hierarchic and quantitative intelligent diagnosis model of TCM syndromes of hypertension is constructed, which realizes the intelligent classification and quantification of TCM syndrome types of hypertension, based on the accurate analysis of the subtle differences in the color features of each facial region of different TCM syndrome types of hypertension.

 

Results: Syndrome identification accuracy and classification accuracy of overabundant liver-fire syndrome, Yin deficiency with Yang hyperactivity, Yin and Yang deficiency syndrome, and healthy persons in the test respectively reach 68%/74%, 75%/72%, 70.8%/72%, 80%/-, with a good identification effect, but that of excessive accumulation of phlegm-dampness only reach 58.3%/66%, whose model identification effect is not obvious.

 

Conclusions: For the study of small sample size in the field of intelligent diagnosis of hypertension with TCM syndromes, the diagnosis model based on color spectral decomposition (CSD) and random forest (RF) is more effective than the diagnosis model based on DNN. The CSD algorithm for syndrome color extraction and analysis proposes a new model of intelligent diagnosis of the classification and quantification of TCM syndromes in hypertension. This paper uses indirect methods to measure the syndromes and the evolution of syndromes in a non-wound manner. Judging the degree of target organ damage and home rehabilitation for the patients with hypertension makes this study of great research significance and application prospects.