A New Approach of Fatigue Classification Based on Data of Tongue and Pulse With Machine Learning - Shi, Yulin; Yao, Xinghua; Xu, Jiatuo; Hu, Xiaojuan; Tu, Liping; Lan, Fang; Cui, Ji; Cui, Longtao; Huang, Jingbin; Li, Jun; Bi, Zijuan; Li, Jiacai (Frontiers in Physiology, 2022)
Background: Background: Fatigue is a common and subjective symptom, which is associated with many diseases and suboptimal health status. A reliable and evidence-based approach is lacking to distinguish disease fatigue and non-disease fatigue. This study aimed to establish a method for early differential diagnosis of fatigue, which can be used to distinguish disease fatigue from non-disease fatigue, and to investigate the feasibility of characterizing fatigue states in a view of tongue and pulse data analysis. Methods: Tongue and Face Diagnosis Analysis-1 instrument and Pulse Diagnosis Analysis-1 instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue versus non-disease fatigue. Results: The results showed that all the four classifiers over “Tongue & Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model-derived accuracies based on logistic regression, support vector machine, random forest, and neural network were (85.51 ± 1.87)%, (83.78 ± 4.39)%, (83.27 ± 3.48)% and (85.82 ± 3.01)%, respectively with Area Under Curve estimates of 0.9160 ± 0.0136, 0.9106 ± 0.0365, 0.8959 ± 0.0254 and 0.9239 ± 0.0174, respectively. Conclusion: This study proposed and validate dan innovative, non-invasive differential diagnosis approach. Results suggest that it is feasible to characterize disease fatigue and non-disease fatigue by using objective tongue data and pulse data.