A Study of Logistic Regression for Fatigue Classification Based on Data of Tongue and Pulse. (5th March 2022)

• Record Type: Journal Article
• Authors: Shi, Yu Lin; Jiang, Tao; Hu, Xiao Juan; Cui, Ji; Cui, Long Tao; Tu, Li Ping; Yao, Xing Hua; Huang, Jing Bin; Xu, Jia Tuo
• Other Names: Qiao Haifa Academic Editor.
• Is Part Of: Evidence-based complementary and alternative medicine. Volume 2022(2022)
• Publication Date: 2022-03-05
• Subjects: Alternative medicine -- Periodicals; 615.505
• DOI: 10.1155/2022/2454678
• Languages: English
• ISSNs: 1741-427X

Abstract

Abstract : Background and Objective . Fatigue is a subjective symptom which is hard to quantify, it is prevalent in the subhealth and disease population, and there is still no accurate and stable method to distinguish disease fatigue from subhealth fatigue. Tongue diagnosis and pulse diagnosis are the reflection of the overall state of the body, and the modern research of tongue diagnosis and pulse diagnosis has made great progress. This study aims to explore the distribution rules of tongue and pulse data in a disease fatigue and subhealth fatigue population and evaluate the contribution rate of tongue and pulse data to fatigue diagnosis through modeling. Methods . The Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect the tongue image and sphygmogram of the subhealth fatigue population ( n = 252) and disease fatigue population ( n = 1160), and we mainly analyzed the tongue and pulse characteristics and constructed the classification model by using the logistic regression method. Results . The results showed that subhealth fatigue people and disease fatigue people had different characteristics of tongue and pulse, and the logistic regression model based on tongue and pulse data had a good classification effect. The accuracies of models of healthy controls and subhealth fatigue, subhealth fatigue and disease fatigue, and healthy controls and disease fatigue were 68.29%, 81.18%, and 84.73%, and the AUC was 0.698, 0.882, and 0.924, respectively. Conclusion . This study provided a new noninvasive method for the fatigue diagnosis from the perspective of objective tongue and pulse data, and the modern tongue diagnosis and pulse diagnosis have good application prospects.