In:
Frontiers in Physiology, Frontiers Media SA, Vol. 12 ( 2022-2-7)
Abstract:
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 (TFDA-1) instrument and Pulse Diagnosis Analysis-1 (PDA-1) instrument were used to collect tongue and pulse data. Four machine learning models were used to perform classification experiments of disease fatigue vs. non-disease fatigue. Results The results showed that all the four classifiers over “Tongue & amp; Pulse” joint data showed better performances than those only over tongue data or only over pulse data. The model accuracy rates 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)%, and 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 validated an 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.
Type of Medium:
Online Resource
ISSN:
1664-042X
DOI:
10.3389/fphys.2021.708742
DOI:
10.3389/fphys.2021.708742.s001
DOI:
10.3389/fphys.2021.708742.s002
DOI:
10.3389/fphys.2021.708742.s003
DOI:
10.3389/fphys.2021.708742.s004
DOI:
10.3389/fphys.2021.708742.s005
DOI:
10.3389/fphys.2021.708742.s006
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2564217-0
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