Constitution identification model in traditional Chinese medicine based on multiple features
10.1016/j.dcmed.2024.09.002
- VernacularTitle:基于多特征的中医体质辨识模型研究
- Author:
Anying XU
1
;
Tianshu WANG
;
Tao YANG
;
Xiao HAN
;
Xiaoyu ZHANG
;
Ziyan WANG
;
Qi ZHANG
;
Xiao LI
;
Hongcai SHANG
;
Kongfa HU
Author Information
1. 南京中医药大学人工智能与信息技术学院,江苏南京 210023,中国
- Keywords:
Traditional Chinese medicine(TCM);
Constitution identification;
Deep feature;
Facial complexion feature;
Body shape feature;
Multiple features
- From:
Digital Chinese Medicine
2024;7(2):108-119
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a precise model for identifying traditional Chinese medicine(TCM)constitutions,thereby offering optimized guidance for clinical diagnosis and treatment plan-ning,and ultimately enhancing medical efficiency and treatment outcomes. Methods First,TCM full-body inspection data acquisition equipment was employed to col-lect full-body standing images of healthy people,from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire(CCMQ),and a dataset encompassing labelled constitutions was constructed.Second,heat-suppres-sion valve(HSV)color space and improved local binary patterns(LBP)algorithm were lever-aged for the extraction of features such as facial complexion and body shape.In addition,a dual-branch deep network was employed to collect deep features from the full-body standing images.Last,the random forest(RF)algorithm was utilized to learn the extracted multifea-tures,which were subsequently employed to establish a TCM constitution identification mod-el.Accuracy,precision,and F1 score were the three measures selected to assess the perfor-mance of the model. Results It was found that the accuracy,precision,and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842,0.868,and 0.790,respectively.In comparison with the identification models that encompass a single feature,either a single facial complexion feature,a body shape feature,or deep features,the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105,0.105,and 0.079,the precision increased by 0.164,0.164,and 0.211,and the F1 score rose by 0.071,0.071,and 0.084,respectively. Conclusion The research findings affirmed the viability of the proposed model,which incor-porated multifeatures,including the facial complexion feature,the body shape feature,and the deep feature.In addition,by employing the proposed model,the objectification and intel-ligence of identifying constitutions in TCM practices could be optimized.
