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Journal of Chinese Integrative Medicine ›› 2012, Vol. 10 ›› Issue (9): 975-982.doi: 10.3736/jcim20120905

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Analysis of syndrome discipline of generalized anxiety disorder using data mining techniques

Qi-sheng Tang1(), Wen-jun Sun1, Miao Qu1, Dong-fang Guo2   

  1. 1. Department of Encephalopathy, Third Affiliated Hospital, Beijing University of Chinese Medicine, Beijing 100029, China
    2. Department of Neurology, General Hospital of Armed Police Forces, Beijing 100039, China
  • Received:2012-02-10 Accepted:2012-04-26 Online:2012-09-20 Published:2018-09-15
  • Contact: Qi-sheng Tang E-mail:tangqisheng@263.net

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Objective: To study the use of data mining techniques in analyzing the syndrome discipline of generalized anxiety disorder (GAD).

Methods: From August 1, 2009 to July 31, 2010, 705 patients with GAD in 10 hospitals of Beijing were investigated over one year. Data mining techniques, such as Bayes net and cluster analysis, were used to analyze the syndrome discipline of GAD.

Results: A total of 61 symptoms of GAD were screened out. By using Bayes net, nine syndromes of GAD were abstracted based on the symptoms. Eight syndromes were abstracted by cluster analysis. After screening for duplicate syndromes and combining the experts’ experience and traditional Chinese medicine theory, six syndromes of GAD were defined. These included depressed liver qi transforming into fire, phlegm-heat harassing the heart, liver depression and spleen deficiency, heart-kidney non-interaction, dual deficiency of the heart and spleen, and kidney deficiency and liver yang hyperactivity. Based on the results, the draft of Syndrome Diagnostic Criteria for Generalized Anxiety Disorder was developed.

Conclusion: Data mining techniques such as Bayes net and cluster analysis have certain future potential for establishing syndrome models and analyzing syndrome discipline, thus they are suitable for the research of syndrome differentiation.

Key words: data mining, questionnaires, Bayes theorem, cluster analysis, anxiety disorder, syndrome discipline

Figure 1

Bayes net model between symptoms of generalized anxiety disorder"

Figure 2

Bayes net model between syndrome elements and syndrome targets of generalized anxiety disorder"

Table 1

Combination of syndrome patterns drawn from Bayes net model"

Syndrome Combination style
Depressed liver qi transforming into fire Heart — liver — fire-heat — stagnation of qi
Qi stagnation and blood stasis Qi stagnation — liver — fire-heat — blood stasis
Phlegm-heat harassing the heart Spleen — phlegm — heart — fire-heat
Deficiency of heart qi and gallbladder qi Heart — qi deficiency — gallbladder — yang deficiency
Liver depression and spleen deficiency Liver — qi stagnation — phlegm — spleen — qi deficiency
Heart-kidney non-interaction Heart — fire-heat — blood deficiency — kidney — yin deficiency
Dual deficiency of the heart and spleen Heart — fire-heat — blood deficiency — phlegm dampness — spleen — qi deficiency
Kidney essence deficiency Kidney — yang deficiency — essence deficiency
Kidney deficiency and liver yang hyperactivity Liver — fire-heat — blood deficiency — kidney — yang deficiency — essence deficiency

Figure 3

Cluster analysis model between syndrome elements and syndrome targets of generalized anxiety disorder "

Table 2

Procedure of systematic cluster analysis"

Step Cluster combination Correlation
coefficient		 Step cluster first appeared Next step
Cluster 1 Cluster 2 Cluster 1 Cluster 2
1 Heart fire hyperactivity Liver fire hyperactivity 0.959 0 0 10
2 Kidney yang deficiency Kidney essence deficiency 0.951 0 0 9
3 Heart yang deficiency Deficiency of gallbladder qi 0.922 0 0 5
4 Heart blood deficiency Heart yin deficiency 0.898 0 0 6
5 Heart yang deficiency Spleen yang deficiency 0.886 3 0 11
6 Heart qi deficiency Heart blood deficiency 0.882 0 4 10
7 Liver qi depression Liver yin deficiency 0.844 0 0 12
8 Spleen qi deficiency Phlegm 0.843 0 0 11
9 Kidney yang deficiency Kidney yin deficiency 0.801 2 0 15
10 Heart fire hyperactivity Heart qi deficiency 0.797 1 6 12
11 Heart yang deficiency Spleen qi deficiency 0.671 5 8 14
12 Heart fire hyperactivity Liver qi depression 0.655 10 7 13
13 Heart fire hyperactivity Blood stasis 0.563 12 0 14
14 Heart fire hyperactivity Heart yang deficiency 0.503 13 11 15
15 Heart fire hyperactivity Kidney yang deficiency 0.460 14 9 0

Table 3

Combination of syndrome patterns drawn from the cluster analysis model"

Syndrome Combination style
Depressed liver qi transforming into fire Heart fire hyperactivity — liver fire hyperactivity
Kidney essence deficiency Kidney essence deficiency — deficiency of kidney yang
Deficiency of heart qi and gallbladder qi Heart yang deficiency — gallbladder qi deficiency
Heart blood deficiency Heart blood deficiency — heart yin deficiency
Both qi and blood deficiency Heart qi deficiency — heart blood deficiency
Spleen qi deficiency with phlegm Spleen qi deficiency — phlegm
Both yin and yang deficiency Kidney yin deficiency — kidney yang deficiency
Qi depression and blood stasis Liver qi depression — blood stasis
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