Syndrome differentiation of fatty liver based on the whole network analysis theory

Objective: Investigate syndromes classification of fatty liver. Method: Investigate the relationship between syndrome differentiation and symptom of fatty liver by using SOM neural network and whole network analysis method, and provide references for the standardization of syndrome differentiation. Analysis on centrality was carried out to evaluate the importance of each symptom in each syndrome differentiation. Analysis on group centralization was carried out to speculate the amount of the syndrome types and the accompanied degree of symptoms. Analysis on E-I index was carried out to speculate the reliability of the syndrome differentiation. Subgroup analysis was used to provide a reference for fatty liver syndromes. Result: The syndromes of fatty liver were found to be complex cluster syndromes rather than simple single syndromes. Conclusion: Analysis of the relationship between different symptoms of fatty liver revealed a conspicuous Chinese medicine syndromes colonization concept. Standardization of syndrome differentiation of fatty liver is of great importance for its high reference value. The analysis method based on a complex cluster syndromes database was proven feasible.