A Study On Fatigue Classification By Logistic Regression Method: Based On Data of Tongue And Pulse

Abstract Purpose: Fatigue is a subjective symptom which is hard to quantify, it is prevalent in sub-health and disease population, and there is still no accurate and stable method to distinguish disease fatigue from sub-health fatigue. Tongue and pulse diagnoses are the reflection of the overall state of the body, and the modern researches of tongue and pulse diagnoses have made great progress. This study aims to explore the distribution rules of tongue and pulse data in disease fatigue and sub-health fatigue population, and evaluate the contribution rate of tongue and pulse data to fatigue diagnosis through modeling. Methods: Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect tongue image and pulse sphygmogram of sub-health fatigue population (n=252) and disease fatigue group(n=1160), we mainly analyzed the tongue and pulse characteristics and constructed the classification model based on logistic regression method. Results: The results showed that sub-health fatigue people and disease fatigue people had different characteristics of tongue and pulse, and the logistic regression model based on tongue and pulse data showed a better classification effect. The accuracy of healthy controls & sub-health fatigue, sub-health fatigue & disease fatigue, health controls & disease fatigue model were 65.70%, 65.10%, 78.90%, and the AUC were 0.678, 0.834, and 0.879 respectively. Conclusion: This study provided a new non-invasive method for the fatigue diagnosis from the perspective of objective tongue and pulse data, the modern tongue and pulse diagnoses have a good application prospect.