Constitution identification model in traditional Chinese medicine based on multiple features
10.1016/j.dcmed.2024.09.002
- Author:
XU Anying
1
;
WANG Tianshu
1
;
YANG Tao
1
;
HAN Xiao
1
;
ZHANG Xiaoyu
2
;
WANG Ziyan
1
;
ZHANG Qi
1
;
LI Xiao
3
;
SHANG Hongcai
3
,
4
;
HU Kongfa
1
,
5
,
5
,
5
,
6
,
7
,
8
,
9
Author Information
1. School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
2. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
3. Key Laboratory of Internal Medicine of Chinese Medicine, Ministry of Education, Beijing University of Chinese Medicine, Beijing 100700, China
4. Dongfang Hospital, Beijing University of Chinese Medicine, Beijing 100078, China
5. Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing, Jiangsu 210023, China
6. ce and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
7. gy, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
8. Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
9. Jiangsu Research Center for Major Health Risk Management and TCM Control Policy, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China
- Publication Type:Journal Article
- 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:English
-
Abstract:
Objective:To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes.
Methods:First, TCM full-body inspection data acquisition equipment was employed to collect 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-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged 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 multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model.
Result: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 incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.
- Full text:2024100815474844221hukongfa.docx
