Investigating the pattern of syndrome based on the difference of symptom network in depression

In TCM theory, the syndrome is crucial to diagnose diseases and treat patients. In syndrome identification, the relation of symptoms usually correlates with syndrome and represents the pattern of syndrome at symptomatic level. Hence, we learn models for classifying syndromes in depression using 4 different algorithms, which are naive Bayes, Bayes network, SVM and C4.5. From the results of classification, we find that the dependence of symptoms has something to do with the accuracies of syndrome classification. Then, 8 symptom networks corresponding to depression and 7 syndromes are constructed to explore the interaction profile of symptoms under syndrome. By comparing syndrome-specific symptom network to the base network of depression, we discover the enriched edges and different nodes to represent the pattern of each syndrome. Literature and symptom ranking by Fisher score demonstrate the correctness of the different nodes selected through network comparison. After all, the enriched edges and different nodes associated with a given syndrome reveal the pattern of that syndrome at symptomatic level.